Web Design News Archives - Page 67 of 120

Winning the platform shift by Braze

November 4, 2025/0 Comments/in Search Engine Land, Web Design News/by

Grappling with innovation and changing consumer attitudes is second nature to marketers, who have already lived through many technological shifts over the past two decades. But forecasting where things are going is especially hard when it comes to modern AI, which has such unusual, non-deterministic properties. You can’t just extrapolate from the state of AI today to understand where AI is going to be in five years (or one…); during this sort of a platform shift, you need to take a deeper first-principles look.

Some things won’t change. Consumers will always want products, services and experiences that resonate and meet their needs. Marketers will always want easier, faster and more effective ways to connect with consumers. But the technologies that mediate that relationship are primed to shift in the coming years in major, unprecedented ways — impacting how marketers do their work, and the customer experiences they’re able to deliver.

How the marketer experience will evolve: Less rote work, more creativity

The history of marketing is built around constant evolution. But the scale and complexity of the change triggered by the rise of modern AI may test even seasoned customer engagement teams. To thrive, marketers need to open themselves up to new skills, perspectives and capabilities that will allow them to do more with less.

This change is already underway. As marketers take advantage of AI, they’re spending less time on rote tasks (like manual message creation) and more on strategy and creative work — from brainstorming innovative campaigns to deepening their testing and optimization strategy. These efficiency gains will grow as AI becomes a more prominent part of the customer engagement process, allowing brands to set goals and guardrails, then empowering their AI solutions to independently consume context, make decisions, and act on marketers’ behalf.

Today, that might look like training basic agents on your brand’s voice to ensure that message content is consistently on brand. But as we gain trust in AI’s ability to operate unsupervised over longer time horizons and to handle complex projects, more marketers will be able to shift their focus to strategy and effective management of the AI resources at their disposal to enable AI decisioning and other essential optimizations.

How team experiences will evolve: Humans and AI agents working side by side

Marketing is a collaborative art, where building a successful customer engagement program often depends as much or more on marketers’ ability to work together effectively as it does on their individual skills. But while AI may help marketers to work with internal stakeholders more effectively, its biggest unlock is the ability to be a direct “teammate” to marketers themselves. And by leveraging AI’s ability to create countless agents that can support customer engagement, even entry-level marketers will likely find themselves essentially operating as a “manager” of a team of autonomous subordinates.

Imagine creating a whole team of agents, with one tasked with personalizing product recommendations, one that QAs messages to ensure they’re formatted and built correctly, one that handles translations and another that reports back at the first sign of campaign underperformance. By supplementing your existing capabilities with agents, you aren’t just reducing the burden on yourself and your human colleagues; you’re also building a digital institutional memory, training these “teammates” with context and goals and reward functions to be able to keep supporting your efforts and driving value even as human coworkers come and go and your team’s goals shift and evolve with time.

AI and customer engagement: How brands can win the future

For years, marketers have sought the ability to truly personalize communication on a 1:1 basis across an audience of millions, and to do it swiftly, efficiently and at scale. This was the Holy Grail of marketing, but due to the limitations of technology it simply wasn’t achievable for even the most advanced teams. That’s all being made possible by AI decisioning, a powerful new type of functionality that can force multiply brands’ marketing performance and creative impact while delivering what their customers want and need.

Previously, a brand trying to win back lapsing customers had a long journey ahead of it. It might start by leveraging a churn propensity model to identify which customers are most likely to churn, then use a product prediction model to figure out what products to highlight in order to tempt them to return. From there, they’d need to run a series of A/B tests in order to figure out which offers and channels will work best. But while taking that approach is a traditional best practice, it only got brands so far — they could target micro-segments on the right channel with the right offer, but truly 1:1 engagement was still out of reach.

AI decisioning represents a new way forward when it comes to personalization. This approach leverages reinforcement learning, where AI agents learn from consumer behavior and learn over time how to maximize rewards (such as conversions or purchases) in order to optimize the KPIs that have the biggest impact through ongoing, autonomous experimentation. That means AI decisioning can seamlessly determine not only the next best product offer for those lapsing users, but also the best channel, the optimal time of day or day of week, the frequency that makes the most sense, the message most likely to drive ideal outcomes, and any other dimension that could impact whether a recipient takes a given action.

Even better, because AI agents are constantly experimenting in the background, the model can continuously adapt to shifting consumer preferences and behavior. And because these models use first-party data about every available customer characteristic, AI decisioning makes it possible to engage with individuals in a true 1:1 way, rather than relying on segments. The result is exceptional relevance and responsive experiences for individual consumers, something that’s only possible because of AI.

Final thoughts

With any major technology shift, it isn’t enough to just plan for the obvious outcomes — you must ensure you can react effectively to the changes that no one knows are coming. To succeed, brands need to pay careful attention to the arc of this new technology. Responding to a platform shift can’t be a one-and-done thing, and brands that create a five-year plan without building in regular pulse points and adjustments are going to quickly find themselves falling behind their more agile, flexible peers.

To see the full benefit of AI in their customer engagement efforts, brands also need to look beyond AI. After all, AI isn’t a shortcut, it’s an amplifier — and the AI you use for customer engagement is only ever going to be as good as the infrastructure supporting it. An exceptional AI feature isn’t going to feel exceptional to consumers if it’s built on architecture that can’t take action in real time or can only deliver experiences in a single, prescribed way. Make sure your AI tools are built on a strong foundation and have the infrastructure they need to shine; otherwise, you may never fully achieve what’s possible.

Curious to learn more about how Braze is thinking about AI and customer engagement? Check out our BrazeAIᵀᴹ page.

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Does Your Website Need an LLMs.txt File? + How to Create One

November 4, 2025/0 Comments/in Web Design News/by

There’s been a lot of buzz around llms.txt.

But no major AI platform has confirmed that they use it.

Not yet, anyway.

And there’s no evidence that any major large language model (LLM) actually uses it when crawling.

So, why are some SEOs and site owners already adding it to their sites?

Because LLM traffic is projected to explode over the next few years.

Google and LLM Unique Visitor Growth Projection (Moderate Case)

Which means AI models could soon become your biggest traffic source.

Remember: robots.txt was once optional, too.

Today, it’s essential for managing search crawlers.

LLMs.txt could follow a similar path — becoming the standard way to guide AI to your most important content.

In this guide, you’ll learn how llms.txt files work, the key pros and cons, and the exact steps to create one for your site.

You’ll also see different llms.txt examples from real sites.

First up: a quick explainer.

What Is LLMs.txt?

LLMs.txt is a plain-text file that tells AI models which pages to prioritize when crawling your site.

This proposed standard could make your content easier for AI systems to find, process, and cite.

Here’s how it works:

You create a text file called llms.txt
List your most important pages with brief descriptions of what each covers
Place it at your site’s root directory
In theory, LLM crawlers would then use the file to discover, prioritize, and better understand your key pages

For example, here’s what Yoast SEO’s llms.txt file looks like:

Does LLMs.txt Replace Robots.txt?

Short answer: No.

They serve different purposes.

Robots.txt tells crawlers what they’re allowed to access on a site.

It uses directives like “Allow” and “Disallow” to control crawling behavior.

LLMs.txt suggests which pages AI models should prioritize.

It doesn’t control access — it just provides a curated list. And makes it easier for crawlers to understand your content.

For example, you might use robots.txt to block crawlers from your admin dashboard and checkout pages.

Then, use llms.txt to point AI systems toward your help docs, product pages, and pricing guide.

Here’s a full breakdown of the differences:

	LLMs.txt	Robots.txt
Purpose	Provides a curated list of key pages that AI models may use for information and sources	Sets rules for search engine crawlers on what to crawl and index
Target audience	LLMs like ChatGPT, Gemini, Claude, Perplexity	Traditional search engine bots (Googlebot, Bingbot, etc.)
Syntax	Markdown-based; human-readable	Plain text, specific directives
Enforcement	Proposed standard; adherence is not confirmed by major LLMs	Voluntary; considered standard practice and respected by major search engines
SEO/AI impact	May influence AI-generated summaries, citations, and content creation	Directly impacts search engine indexing and organic search rankings

Layout and Elements

So, what goes inside this file — and how should you structure it?

LLMs.txt should be created as a plain-text file and formatted with markdown.

Markdown uses simple symbols to structure content.

This includes:

# for a main heading, ## for section headings, ### for subheads
> to call out a short note or tip
– or * for bullet lists
[text](https://example.com/page) for a labeled link
Triple backticks (“`) to fence off code examples when you’re showing snippets in a doc or blog post

Yoast – LLMs.txt with a #heading and list

This makes the file easy for both humans and AI tools to read.

You can see the main elements in this llms.txt example:

# Title
> Description goes here (optional)
Additional details go here (optional)
## Section
- [Link title](https://link_url): Optional details
## Optional
- [Link title](https://link_url)

Now that you know how to format the file, let’s break down each part:

Title and optional description at the top: Add your site or company name, plus a brief description of what you do to give AI systems context
Sections with headers: Organize content by topic, like “Services,” “Case Studies,” or “Resources,” so crawlers can quickly identify what’s in the file
URLs with short descriptions: List key pages you want prioritized. Use clear, descriptive SEO-friendly URLs. And add a concise description after each link for context.
Optional sections: Consider adding lower-priority resources you want AI systems to be aware of but don’t need to emphasize — like “Our Team” or “Careers”

To put all the pieces together, let’s look at some examples.

Here’s how BX3 Interactive, a website development company, structures its llms.txt file:

It features:

The company’s name
Brief description
List of key service pages with URLs and one-sentence summaries
Top projects and case studies
Citation and linking guidelines

BX3 Interactive also includes target terms and specific CTAs for each URL.

If adopted, this approach could shape how LLMs reference the brand, guiding them toward BX3 Interactive’s preferred messaging and phrasing.

LLMs.txt files can also be more complex, depending on the site.

Like this example from the open-source platform Hugging Face:

It organizes hundreds of pages with nested headings to create a clear hierarchy.

But it goes well beyond URL lists and summaries.

It includes:

Step-by-step installation commands
Code examples for common tasks
Explanatory notes and references

This way, AI systems would get direct access to Hugging Face’s most valuable documentation without needing to crawl every page.

This could reduce the risk of key details getting missed or buried.

Keep in mind that the ideal structure depends on the scope of your site. And the depth of information you want AI to understand.

Further reading: Vibe Marketing: Hype, Reality, and Real Case Studies

Is LLMs.txt Worth It?

The jury is out.

It’s possible that an llms.txt file could boost your AI SEO efforts over time.

But that would require widespread adoption.

No major AI platform has officially supported the use of llms.txt yet.

And Google has been especially clear — they don’t support it and aren’t planning to.

LinkedIn – Kenichi Suzuki – Use of LLMs.txt

But big players like Hugging Face and Stripe already have llms.txt files on their sites.

Most notably, Anthropic, the company behind Claude, also has an llms.txt file on its website.

If one of the leading AI companies is using it themselves, it could mean they see potential for these files to play a bigger role in the future.

Note: While Anthropic has an llms.txt file on its site, it hasn’t publicly stated that its crawlers use or read these files.

Bottom line?

Treat llms.txt as a low-risk experiment, not a guaranteed way to boost AI visibility.

Potential Benefits

Right now, the benefits are theoretical.

But if llms.txt catches on, you could benefit in multiple ways:

Control what gets cited: Spotlight your blog posts, help docs, product pages, and policies so AI tools reference your best pages first instead of less important or outdated content
Make parsing easier: Your llms.txt file gives AI models clean markdown summaries instead of forcing them to parse through cluttered pages with navigation, ads, and JavaScript
Improve your AI performance: Guide AI models to your most valuable pages, potentially improving how often and accurately they cite your content in responses
Analyze your site faster: A flattened version of your site (a single, simplified file listing your key pages), makes it easier to run a keyword analysis and site audit without crawling every URL

Key Limitations and Challenges

The skepticism around llms.txt is valid.

Here are the biggest concerns:

No one’s officially using it yet: No major platforms have announced support for these files — not OpenAI, Google, Perplexity, or Anthropic
It’s a suggestion, not a rule: LLMs don’t have to “obey” your file, and you can’t block access to any pages. Need access control? Stick with robots.txt.
Easy to game: A separate markdown file creates an opportunity for spam. For example, site owners could overload it with keywords, content, and links that don’t align with their actual pages. Basically, keyword stuffing for the AI era.
You’re showing competitors your hand: A detailed llms.txt file hands your competitors a lot of info they might have to use dedicated tools to get otherwise. Your site structure, content gaps, messaging, keywords, and more.

Further reading: 6 AI Content Tools Worth Using

How to Create an LLMs.txt File in 5 Easy Steps

Creating an llms.txt file is pretty simple — even if you don’t have much technical experience.

One caveat: You may need a developer’s help to upload it.

Step 1: Pick Your High-Priority Pages

Start by selecting the pages you want AI systems to crawl first.

Pro tip: Don’t dump your whole sitemap into your llms.txt file. Focus on your most valuable pages — not an exhaustive inventory.

Think about the evergreen content that best represents what you do — your core product pages, high-value guides, FAQ sections, key policies, and pricing details.

For example, BX3 Interactive lists this web development service page first in its llms.txt file:

Why? Because it’s a core service they offer.

And by featuring it in llms.txt, they’re signaling to AI crawlers that this page is central to their business.

Step 2: Create Your File

Next, open any plain-text editor and create a new file called llms.txt.

Options include Notepad, TextEdit (on Mac), and Visual Studio Code.

Pro tip: Don’t just list bare URLs. Add a brief description for each one that explains what the page covers and who it’s for. This context could help AI understand when and how to cite your brand.

Not comfortable with markdown formatting?

Ask your developer to handle it (if you have one).

Or let an LLM do the work — ChatGPT and Claude can generate these files instantly.

Here’s a prompt to get you started:

Create an llms.txt file in markdown format using this information:

Company Name: [Your Company Name]

Company Description: [One sentence about what you do]

Important Notes (optional):

[Key differentiator or important detail]

[What you do or don’t do]

[Another key point]

Products/Services

URL: [https://yoursite.com/product-1]

Description: [What it does and who it’s for]

URL: [https://yoursite.com/product-2]

Description: [What it does and who it’s for]

Blog/Resources

URL: [https://yoursite.com/blog-post-1]

Description: [What readers will learn]

URL: [https://yoursite.com/blog-post-2]

Description: [What readers will learn]

Company Pages

About: [https://yoursite.com/about] – [Company background and mission]

Contact: [https://yoursite.com/contact] – [How to reach you]

Pricing: [https://yoursite.com/pricing] – [Plan overview]

Format this as a proper llms.txt file with markdown headings (#, ##), bullet points (-), and link syntax.

There are also llms.txt generators you can use.

For example, Yoast SEO lets you generate an llms.txt file in one click, complete with markdown.

Remember, the structure isn’t set in stone.

Include your most valuable pages, accompanied by descriptive summaries.

Then, customize the layout based on what matters most for your company.

Step 3: Upload the File

Where your llms.txt file goes depends on what it covers.

For a site-wide file, upload it to your root directory: https://[yoursite].com
For documentation only, place it in its respective subdirectory: https://[docs.yourdomain.com]/llms.txt

You might need a developer’s help for this next step.

They’ll log in to your hosting panel, navigate to your public_html folder, and upload the file.

Once it’s uploaded, you’re ready to test.

Step 4: Make Sure It Works

Open a new tab and type in https://yoursite.com/llms.txt.

If you see something like this, you’re set:

Want to go a step further?

Use Semrush’s Site Audit tool to verify the file is crawlable and automatically check for any technical issues.

Step 5: Keep It Fresh

Your llms.txt isn’t a set-it-and-forget-it file.

Schedule a review every few months:

Remove outdated pages that no longer represent your best work
Add new content worth spotlighting as it’s published

This ensures AI systems always see your most relevant content.

Further reading: LLM Seeding: A New Strategy to Get Mentioned and Cited by LLMs

Should You Use an LLMs.txt File on Your Site?

As SEOs like to say, “it depends.”

If setup is quick and you’re curious to experiment, it’s worth doing.

Worst case, nothing changes.

Best case, you’re ahead of the curve if AI platforms start paying attention.

In the meantime, don’t neglect proven SEO fundamentals.

Structured data, high-authority backlinks, and helpful content are what help AI — and traditional search engines — understand, trust, and surface your pages.

Want to boost your AI visibility now?

Check out our AI search guide for a framework that’s already working.

The post Does Your Website Need an LLMs.txt File? + How to Create One appeared first on Backlinko.

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Google adds Chrome Web Store user agent

November 3, 2025/0 Comments/in Search Engine Land, Web Design News/by

Google has added a new user agent to its help documentation named Google-CWS. This is the Chrome Web Store user agent that is a user-triggered fetchers.

More details. Google posted about the new user agent over here, it reads; “The Chrome Web Store fetcher requests URLs that developers provide in the metadata of their Chrome extensions and themes.”

What are user-triggered fetchers. A user-triggered fetchers are initiated by users to perform a fetching function within a Google product.

The example provided by Google was “Google Site Verifier acts on a user’s request, or a site hosted on Google Cloud (GCP) has a feature that allows the site’s users to retrieve an external RSS feed. Because the fetch was requested by a user, these fetchers generally ignore robots.txt rules. The general technical properties of Google’s crawlers also apply to the user-triggered fetchers.”

Why we care. If you see this user agent in your crawl logs, you now know where it is from. The Chrome Web Store fetcher requests URLs that developers provide in the metadata of their Chrome extensions and themes.

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7 focus areas as AI transforms search and the customer journey in 2026

November 3, 2025/0 Comments/in Search Engine Land, Web Design News/by

Search is changing faster than ever – and 2026 may be the year it fully breaks from the past.

Over the last year, AI has reshaped how people discover, decide, and convert, collapsing the traditional customer journey and cutting touchpoints in half.

AI-powered assistants and large language models (LLMs) will handle roughly 25% of global search queries by 2026, per Gatner, replacing many traditional search interactions.

We’re already seeing the effects. Traffic from LLMs is climbing at a hockey-stick pace, signaling a massive shift in how users find information.

To stay competitive, marketers need to build strong content and experience flywheels, as answer engine optimization (AEO) and generative engine optimization (GEO) become critical priorities.

Bruce Clay, founder and president of Bruce Clay Inc., predicted:

“AI-powered search is expected to cause traffic to continue to drop for many sites, creating a disturbance in the force.”

Adopting AI isn’t optional – it’s foundational.

Yet most marketing systems weren’t designed to operate in an AI-first world.

Disconnected tools and data silos make orchestration difficult and create inconsistencies that derail performance.

To succeed in 2026, brands will need integrated, cross-functional, omnichannel systems that connect data, content, and customer experience.

Building a resilient digital presence for 2026

Preparing a brand’s digital presence for an AI-driven world means rethinking data, tools, and customer experiences while presenting a clear, consistent brand story.

The goal is to deliver personalized content and be ready for agentic experiences, where AI assistants act on behalf of users.

This shift begins with the evolution of search itself.

The biggest change is moving away from a simple query-and-response model to a more dynamic, reasoning-driven conversation.

Traditional search was like a game of chess – discrete and predictable. AI search, on the other hand, is more like a jazz concert – continuous and fluid.

The experience has shifted from browsing lists and visiting websites to receiving direct, synthesized answers.

Instead of matching keywords to an index, AI uses query fan-out, which involves:

Breaking queries into components.
Analyzing multiple sources.
Delivering a single, comprehensive answer based on consistent patterns.

With AI, the traditional marketing funnel is shrinking. AI search can move directly from intent to conversion in minutes, dramatically accelerating the process.

We’re already seeing three- to eight-times higher conversion rates from traffic originating in AI search.

According to Crystal Carter, head of AI search and SEO communications at Wix:

“Traffic from LLMs (like ChatGPT and Perplexity) is becoming increasingly distinct from Google search traffic, requiring separate optimization and analysis strategies.”

New types of intents, like “generative” (e.g., “create an image”) and “no intent” (e.g., “thanks”), now make up almost half of all LLM interactions and don’t require a website visit.

Search is becoming action-oriented.

As AI systems start booking tables, making appointments, and completing purchases, even transactional journeys may no longer end on your website.

Search ‘everywhere’ optimization: The new SEO

For brands, the goal is no longer to be a single destination. It’s to be present wherever your audience is.

That means becoming a trusted data source that powers the new, agentic ecosystem.

AI systems prioritize clarity, consistency, and patterns, so channel silos must give way to a well-integrated, omnichannel approach.

Ideally, AI agents should be able to access all your brand data and deliver complete, contextually accurate results based on user intent.

As Bill Hunt, president of Back Azimuth Consulting, explained:

“AI agents like ChatGPT will shift from answering questions to completing transactions. Both the Shopify connectors and feeds, as well as Walmart and Amazon saying they are Google killers. Being ‘callable’ through APIs and integrations will be as critical in 2026 as being crawlable was in 2010.”

In this new paradigm, websites are evolving from sales destinations to data and information repositories – built not just for human visitors, but for AI systems that retrieve, interpret, and act on that data.

Dig deeper. Search everywhere optimization: 7 platforms SEOs need to optimize for beyond Google

7 key focus areas shaping marketing and search in 2026

To compete in 2026 and beyond, brands must optimize for visibility across every relevant platform.

Here are seven key priorities and emerging trends shaping the future of search and martech.

1. Strengthen technical SEO foundations for AI retrievability

The foundation of search is shifting from traditional crawlability to GEO.

The core principle of GEO is retrievability – ensuring that high-quality content is not only discoverable but also easily accessible and understood by AI models.

To prepare for this shift, your website should serve as a centralized data hub for your content and digital assets, enhancing the experience for both humans and AI systems.

Make sure to grant access to AI crawlers in your robots.txt file, use server-side rendering (SSR) for core content, and adopt progressive indexing protocols like IndexNow, used by Bing.

2. Build localized visibility in AI-driven environments

Local SEO has evolved – from data accuracy in its 1.0 phase, to profile completeness and engagement in 2.0, to personalized experiences in what’s now emerging as Local 3.0.

AI models, particularly Google’s AI Mode, increasingly cite local business information from sources like Google Maps and online directories.

That makes core local SEO practices – NAP consistency and Google Business Profile optimization – critical for maintaining AI visibility.

Pages with robust schema markup also tend to earn higher citation rates in AI Overviews, reinforcing the importance of structured data for local relevance.

Dig deeper: AI and local search: The new rules of visibility and ROI in 2025

Get the newsletter search marketers rely on.

See terms.

3. Develop an AI-assisted content flywheel

The biggest challenge today isn’t just creating content – it’s creating a connected experience.

As companies integrate AI into their digital experience platforms (DXPs), the focus must shift from producing siloed assets to building a connected content flywheel.

That begins with a deep understanding of who your customers are and what they need, allowing you to fill content gaps in real time and stay present at every critical touchpoint.

DXPs are no longer static repositories. They’re evolving into intelligent, AI-native engines that proactively shape user experiences.

The ideal platform uses AI to create quality content at scale, powering a flywheel that delivers personalized, efficient, and well-governed customer journeys.

This is especially important for large brands and multilocation businesses, where updating hundreds of pages still requires manual, repetitive effort.

Here are the key steps to creating quality content and building a content flywheel.

Insights: Identify customer intent and content gaps

Your content strategy should be guided by real-time customer needs.

Use AI-powered tools to uncover the questions and challenges your audience is trying to solve.

Then analyze your existing content to identify gaps where your brand isn’t providing the right answers.

Creation: Develop deep, AI-structured content

To create content that performs well in AI search, start by assessing AI visibility and user sentiment.

Use AI to scale the development of deep, comprehensive content – always with a human in the loop.

Since AI engines draw from text, images, videos, and charts, your content must be equally diverse.

Just as important, it must be machine-readable so AI systems can synthesize and reason with it.

Prioritize an entity-based SEO strategy to build topical authority, and use comprehensive schema markup to help search engines understand your brand and content context.

Clearly structuring your data also prepares your site for advanced conversational search.

It ensures visibility in the next generation of AI-powered answer engines and readiness for NLWeb, the open protocol spearheaded by Microsoft to make websites conversational.

Dig deeper: Chunk, cite, clarify, build: A content framework for AI search

Distribute

Establish a human-in-the-loop workflow to review, update, and refresh content regularly, keeping it accurate, relevant, and effective in answering user queries.

Publish from a centralized source to maintain consistency across owned channels, and adopt rapid indexing protocols like IndexNow to accelerate discovery and visibility.

Monitor and iterate

Continuously track visibility and performance within AI models by testing target prompts.

Deploy an agile strategy – as you distribute content, monitor results, experiment with new approaches, and refine continuously, the flywheel becomes self-sustaining.

Each cycle feeds fresh insights back into the system, helping your content strategy stay adaptable and build momentum over time.

“AI search engines synthesize across ecosystems, not just pages. Marketing leaders must ensure their digital footprint works as a unified system, not isolated campaigns,” Hunt said.

Businesses must maintain consistent, clear information across every channel.

Traditional SEO is giving way to relevance engineering – a discipline centered on systematically creating and structuring content for semantic relevance.

This approach helps brands navigate today’s increasingly complex query landscape.

4. Create a consistent, data-driven experience flywheel

While the content flywheel attracts visitors, the experience flywheel converts them – a critical function in an era of zero-click searches. It operates on a continuous feedback loop.

Strategy: Building an experience strategy starts with unified data from every customer touchpoint and channel. AI can segment this data to reveal audience expectations and friction points, helping shape a strategy grounded in real behavior.
Experience: AI can then put this data to work – connecting audience intent, personas, desired outcomes, and business goals to generate predictive insights that drive personalized and agentic experiences dynamically.
Conversion: AI also helps track the customer journey through the funnel across channels and touchpoints. Dynamic A/B testing and conversion rate optimization (CRO) can then be done at scale, tailored to audience segments and intent.
Iteration: The goal isn’t perfection but agility. Monitoring performance alone isn’t enough – iteration matters. Use data to make real-time pivots, refining your strategy with every new learning.

The experience flywheel becomes a self-reinforcing engine that continuously drives engagement, builds loyalty, and accelerates growth.

5. Use AI agents to orchestrate journeys and workflows

As AI-driven search becomes increasingly agentic, it establishes a new standard for the seamless digital experiences customers expect.

To meet this demand, brands must use journey orchestration and workflow automation powered by AI agents that guide users through connected, intuitive experiences.

The key is to deploy specialized vertical AI agents trained on your business data.

By orchestrating these agents across the customer journey, you can deliver hyper-personalized, omnichannel experiences.

This is only possible if your website and systems are ready to interact with AI agents.

For internal teams, AI agents also offer major opportunities to automate manual workflows across the entire marketing landscape.

Dig deeper: How AI agents are revolutionizing digital marketing

6. Redefine KPIs for an AI-first performance model

As AI satisfies user intent more directly within search results, traditional metrics like rankings and traffic are losing relevance.

This shift means citation is the new rank, pushing teams to optimize content for retrievability rather than rankability.

As metrics like click-through rate decline in importance, new success indicators are emerging – including LLM visibility score, AI citation count, share of voice, and sentiment.

Success now depends on query diversity, or the ability to answer multiple related long-tail queries effectively.

According to Ray Grieselhuber, CEO of Demandsphere:

“Traditional metrics like impressions, clicks, and click-through rates are becoming much more difficult to rely on as KPIs. They are still useful to look at, but marketers should renew their focus on human behavior. Share of Voice is one of the best KPIs to measure this new behavior. Companies that ignore visibility in AI-driven responses risk ‘feeding that territory’ to their competitors.”

7. Integrate systems and data to power a unified marketing infrastructure

A fragmented marketing tech stack with siloed tools creates inefficiencies and hidden costs.

Data fragmentation and manual processes increase operational expenses and derail integration efforts.

Shifting focus to an integrated marketing platform – and evaluating total cost of ownership – helps overcome these challenges.

An integrated solution provides the consistency, clarity, and unified data needed to keep your digital presence adaptive and competitive.

Dig deeper: Integrating SEO into omnichannel marketing for seamless engagement

The next phase of search and the customer journey

As we move into 2026, AI is not just another tool – it’s rebuilding the customer journey from the ground up.

With AI assistants expected to handle a quarter of all search queries, the traditional marketing funnel is shrinking.

The new landscape is defined by agentic, action-oriented interactions that can bypass websites entirely, demanding a fundamental strategic shift from every brand.

To stay visible and relevant, businesses must evolve from being destinations to being trusted data sources for AI.

That begins by fueling a content flywheel with deep, structured content accessible across every channel.

Once this flywheel attracts an audience, an experience flywheel – powered by unified customer data and an integrated, AI-native platform – takes over to drive conversion through deep personalization.

Ultimately, the brands that succeed will be those that embrace this new ecosystem.

They’ll replace outdated metrics, such as traffic, with new KPIs focused on AI visibility, tear down silos through integration, and prioritize delivering seamless, omnichannel experiences.

Thank you to Bill Hunt, Ray Grieselhuber, Bruce Clay, Crystal Carter, David Banahan, and Tushar Prabhu for their insights and contributions.

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Google says verify your cloud hosting provider with Search Console

November 3, 2025/0 Comments/in Search Engine Land, Web Design News/by

John Mueller from Google posted an SEO tip and reminder for those who use cloud services, such as AWS, Azure, Google Cloud or others, to host images, videos or other content. John explained that you should probably verify those within Google Search Console. This will give you the ability to track the performance of those files in Google Search, including any debugging information when necessary.

Of course, in order to do this, you need to be able to control the DNS and most give you the option to do that through DNS CNAME. So you can set up your DNS to control those files in that cloud environment. For examples, it can be images.domain.com or videos.domain.com and so on.

The advice. Here is John’s post on this on Bluesky:

If you’re using a cloud provider to host images / videos / other content, you can and should verify the host in Search Console, so that you’re aware of potential issues that affect Google’s crawling & indexing, & Safe Browsing. Use a DNS CNAME to the bucket, then verify with DNS.

Using your own hostname (something like content.your-site.com) means you can verify it in Google Search Console to get crawl errors and malware alerts. You can verify using DNS verification… or just verifying your main domain.

To do this, set up a CNAME entry for your domain name and point at your cloud provider’s bucket, eg “content.your-site.com” uses a CNAME for “your-bucket.clodstorage.com” (or “buckets.clodstorage.com”). Also, you will have to update all links in your site (ugh, I know).

You need to update all the links within your site so that users only find your content with your new hostname. For bigger sites, this is a hassle, I know. Search & replace, then double-check by crawling the main sections of your site (all templates, all important URLs).

Caveat: if you need to do this for images, and you care about Image search traffic, know that this will cause fluctuations in Google Images (images are often recrawled slower than web pages and need to be “re-processed” with the new URLs). It’ll settle down though.

Bonus: if you use something like “content.your-site.com”, you can just verify the main domain with DNS in Search Console, and get all data for your website + the content hosted there in a single property in Search Console.

AND THAT’S NOT ALL. IF YOU ORDER NOW, YOU ALSO RECEIVE the ability to migrate to another cloud storage provider without breaking a sweat. Map the CNAME to the new bucket (if the file URLs remain the same), use redirects (it’s your hostname). It’s not really your site unless it’s on your domain name.”

Why we care. It is super common these days for websites to use numerous cloud hosting services and products. So it is totally possible that you are missing out on data, analytics and useful debugging details within Google Search Console for those services.

Verifying them on Search Console should not be a big deal for your site’s administrator and should it should unlock a lot of useful information for you and your SEO team.

Your Competitors Are Already Optimizing for AI Search. Are You?

✓ Monitor how AI platforms rank you vs competitors in real-time

✓ Discover untapped AI visibility opportunities in your industry

✓ Track sentiment shifts across 5+ major AI platforms

See what AI says about your brand today

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Google Local Services Ads vs. Search Ads: Which drives better local leads?

November 3, 2025/0 Comments/in Search Engine Land, Web Design News/by

Google gives local businesses two main ways to generate PPC leads online: Local Services Ads (LSAs) and Search campaigns.

LSAs are pay-per-lead campaigns – for actions such as calls, messages, or booked appointments – with a quick setup process that involves verifying your business. After that, Google automates most of the ad and keyword setup.

Search campaigns are more complex but offer far greater control over ad copy, keywords, and optimization.

Understanding how each format works – and when to use them – can help you get more qualified leads and make smarter use of your ad budget.

Most advertisers use both and shift budgets based on which delivers better long-term results.

Getting started with Google Local Services Ads

LSAs work for businesses of all sizes, not just those with small budgets.

For small business owners, LSAs offer an easy way to set up and run ads quickly.

This is one of the few ad formats where following Google’s setup instructions can actually work well.

That’s not the case for Google Search campaigns, which are far more complex and often waste spend when relying on Google’s automated suggestions.

Small businesses can prepay a few hundred dollars to test results.

While LSAs offer fewer options for control, customization, or optimization, they can work well for very small budgets.

They don’t require as much active management as Search campaigns – though they aren’t completely “set it and forget it” either.

Larger companies can also benefit from testing LSAs alongside other ad formats to compare results.

However, not all industries are eligible, so always confirm availability before allocating budget.

During setup, review all details carefully – including company information, service areas, and specific services – rather than assuming Google configured them correctly.

You have limited control over ad copy and keywords, since Google automatically determines relevant terms.

As Google’s documentation notes, “there is no need to do keyword research as relevant keywords are automatically determined by Google.”

This can work in your favor – or lead to irrelevant traffic – because you can’t define your own keywords.

Reviews are especially important in this format, as they appear prominently and heavily influence results. Collecting legitimate, high-quality reviews is critical for success.

To evaluate performance, connect third-party tools to track and qualify leads.

A basic CRM can help you measure how many leads convert into customers.

Platforms like HouseCall Pro and ServiceTitan can also integrate booking features, letting customers schedule appointments directly through your LSAs.

Dig deeper: Advanced Google Ads tracking for local service companies

Getting more from your Google Search ads

Google Search campaigns are more complex but offer a wider range of features for setup and optimization.

On top of setting business hours, target areas, and other details, Search campaigns give you greater control over ad testing, assets, keywords, match types, bidding strategies, and more.

Testing with just a few hundred dollars is not recommended. These campaigns require active monitoring and frequent optimization to perform well over time.

Unlike LSAs, you can add negative keywords and test a wide range of terms to identify which are most effective and profitable.

A/B testing ad copy and landing pages is also possible, giving Search campaigns much more scalability.

When starting, test a small budget using phrase and exact match keywords only, even with manual CPC bidding to set your maximum bid per click.

This offers tight control for new accounts, though it’s typically a temporary setup before switching to automated bidding and broader match types.

With larger budgets, you can immediately use automated bidding and broad match keywords.

Begin with broad match keywords using a Maximize Conversions bid strategy, then add a target CPA (tCPA) once performance data builds.

In industries with high CPCs, set up portfolio bidding to include both a tCPA and a maximum CPC bid.

Microsoft Ads includes this option natively in its tCPA setting, so portfolio bidding isn’t required there.

After running a Search campaign for two to three months, begin expanding and refining based on performance.

Add new campaigns and ad groups to test additional keyword and ad combinations, aligning each with specific landing pages to maximize lead generation – something not possible with LSAs.

Get the newsletter search marketers rely on.

See terms.

Combining LSAs and Search campaigns for stronger results

As with any advertising channel, it’s essential to regularly evaluate lead quality using a CRM and call tracking tools, such as CallTrackingMetrics or CallRail.

When running both LSAs and Search ads, compare leads from each to assess performance.

LSAs often face lead quality issues, despite being pay-per-lead campaigns.

Google continues improving spam filtering and invalid lead detection for LSAs, but the system still isn’t perfect. Invalid leads can be disputed.

Ad positioning also differs between the two formats. LSAs typically appear at the top of the page, though fewer of them are shown compared to Search ads.

Showing in multiple placements isn’t a problem, but you should continually evaluate cost per lead, lead quality, and lead-to-customer conversion rates for both formats.

Dig deeper: How to expand your reach with reverse location targeting in Google Ads

Expanding beyond LSAs and Search campaigns

For larger budgets, several other Google Ads campaign types are worth testing. These can support lead generation directly or help build local brand awareness.

Display, Video and Demand Gen campaigns can generate leads on their own or build brand awareness for top-of-funnel audiences.

They work well for higher-priced products or services with longer sales cycles, and for lower-priced services that rely on staying top-of-mind – such as plumbing or AC repair.

Performance Max campaigns can also deliver strong lead volume.

However, because they extend beyond Search, it’s essential to monitor lead quality through your CRM and compare it against Search and LSA performance.

With Google Analytics and Google Ads tracking multiple touchpoints before a conversion, you may see fractional conversions.

For example, 0.5 for a Video campaign and 0.5 for a Search campaign – indicating that both contributed to a single lead.

While not a perfect system, this data provides useful context for how different campaigns interact across the customer journey.

Test and compare

Both small and large businesses can benefit from testing LSAs, and all should consider running them alongside Search campaigns to compare results.

There’s no one-size-fits-all approach – both formats can be profitable when properly tracked and optimized.

Dig deeper: Google Ads for SMBs: How to maximize paid search success

Read more at Read More

Google Business Profiles What’s happening feature expands

October 28, 2025/0 Comments/in Search Engine Land, Web Design News/by

Google has expanded the What’s happening feature within Google Business Profiles to restaurants and bars in the United Kingdom, Canada, Australia, and New Zealand. It is now available for multi-location restaurants, not just single-location restaurants.

The What’s happening feature launched back in May as a way for some businesses to highlight events, deals, and specials prominently at the top of your Google Business Profile. Now, Google is bringing it to more countries.

What Google said. Google’s Lisa Landsman wrote on LinkedIn:

How do you promote your “Taco Tuesday” in Toledo and your “Happy Hour” in Houston… right when locals are searching for a place to go?

I’m excited to share that the Google Business Profile feature highlighting what’s happening at your business, such as timely events, specials and deals, has now rolled out for multi-location restaurants & bars across the US, UK, CA, AU & NZ! (It was previously only available for single-location restaurants)

This is a great option for driving real-time foot traffic. It automatically surfaces the unique specials, live music, or events you’re already promoting at a specific location, catching customers at the exact moment they’re deciding where to eat or grab a cocktail.

What it looks like. Here is a screenshot of this feature:

More details. Google’s Lisa Landsman added, “We’ve already seen excellent results from testing and look forward to hearing how this works for you!”

Availability. This feature is only available for restaurants & bars. Google said it hopes to expand to more categories soon. It is also only available in the United States, United Kingdom, Canada, Australia, and New Zealand.

The initial launch was for single-location Food and Drink businesses in the U.S., UK, Australia, Canada, and New Zealand. It is now available for multi-location restaurants, not just single-location restaurants.

Why we care. If you manage restaurants and/or bars, this may be a new way to get more attention and visitors to your business from Google Search. Now, if you manage multi-location restaurants or bars, you can leverage this feature.

Read more at Read More

LLM optimization in 2026: Tracking, visibility, and what’s next for AI discovery

October 28, 2025/0 Comments/in Search Engine Land, Web Design News/by

Marketing, technology, and business leaders today are asking an important question: how do you optimize for large language models (LLMs) like ChatGPT, Gemini, and Claude?

LLM optimization is taking shape as a new discipline focused on how brands surface in AI-generated results and what can be measured today.

For decision makers, the challenge is separating signal from noise – identifying the technologies worth tracking and the efforts that lead to tangible outcomes.

The discussion comes down to two core areas – and the timeline and work required to act on them:

Tracking and monitoring your brand’s presence in LLMs.
Improving visibility and performance within them.

Tracking: The foundation of LLM optimization

Just as SEO evolved through better tracking and measurement, LLM optimization will only mature once visibility becomes measurable.

We’re still in a pre-Semrush/Moz/Ahrefs era for LLMs.

Tracking is the foundation of identifying what truly works and building strategies that drive brand growth.

Without it, everyone is shooting in the dark, hoping great content alone will deliver results.

The core challenges are threefold:

LLMs don’t publish query frequency or “search volume” equivalents.
Their responses vary subtly (or not so subtly) even for identical queries, due to probabilistic decoding and prompt context.
They depend on hidden contextual features (user history, session state, embeddings) that are opaque to external observers.

Why LLM queries are different

Traditional search behavior is repetitive – millions of identical phrases drive stable volume metrics. LLM interactions are conversational and variable.

People rephrase questions in different ways, often within a single session. That makes pattern recognition harder with small datasets but feasible at scale.

These structural differences explain why LLM visibility demands a different measurement model.

This variability requires a different tracking approach than traditional SEO or marketing analytics.

The leading method uses a polling-based model inspired by election forecasting.

The polling-based model for measuring visibility

A representative sample of 250–500 high-intent queries is defined for your brand or category, functioning as your population proxy.

These queries are run daily or weekly to capture repeated samples from the underlying distribution of LLM responses.

Competitive mentions and citations metrics

Tracking tools record when your brand and competitors appear as citations (linked sources) or mentions (text references), enabling share of voice calculations across all competitors.

Over time, aggregate sampling produces statistically stable estimates of your brand visibility within LLM-generated content.

Early tools providing this capability include:

Profound.
Conductor.
OpenForge.

Consistent sampling at scale transforms apparent randomness into interpretable signals.

Over time, aggregate sampling provides a stable estimate of your brand’s visibility in LLM-generated responses – much like how political polls deliver reliable forecasts despite individual variations.

Building a multi-faceted tracking framework

While share of voice paints a picture of your presence in the LLM landscape, it doesn’t tell the complete story.

Just as keyword rankings show visibility but not clicks, LLM presence doesn’t automatically translate to user engagement.

Brands need to understand how people interact with their content to build a compelling business case.

Because no single tool captures the entire picture, the best current approach layers multiple tracking signals:

Share of voice (SOV) tracking: Measure how often your brand appears as mentions and citations across a consistent set of high-value queries. This provides a benchmark to track over time and compare against competitors.
Referral tracking in GA4: Set up custom dimensions to identify traffic originating from LLMs. While attribution remains limited today, this data helps detect when direct referrals are increasing and signals growing LLM influence.
Branded homepage traffic in Google Search Console: Many users discover brands through LLM responses, then search directly in Google to validate or learn more. This two-step discovery pattern is critical to monitor. When branded homepage traffic increases alongside rising LLM presence, it signals a strong causal connection between LLM visibility and user behavior. This metric captures the downstream impact of your LLM optimization efforts.

Nobody has complete visibility into LLM impact on their business today, but these methods cover all the bases you can currently measure.

Be wary of any vendor or consultant promising complete visibility. That simply isn’t possible yet.

Understanding these limitations is just as important as implementing the tracking itself.

Because no perfect models exist yet, treat current tracking data as directional – useful for decisions, but not definitive.

Dig deeper: In GEO, brand mentions do what links alone can’t

Estimating LLM ‘search volume’

Measuring LLM impact is one thing. Identifying which queries and topics matter most is another.

Compared to SEO or PPC, marketers have far less visibility. While no direct search volume exists, new tools and methods are beginning to close the gap.

The key shift is moving from tracking individual queries – which vary widely – to analyzing broader themes and topics.

The real question becomes: which areas is your site missing, and where should your content strategy focus?

To approximate relative volume, consider three approaches:

Correlate with SEO search volume

Start with your top-performing SEO keywords.

If a keyword drives organic traffic and has commercial intent, similar questions are likely being asked within LLMs. Use this as your baseline.

Layer in industry adoption of AI

Estimate what percentage of your target audience uses LLMs for research or purchasing decisions:

High AI-adoption industries: Assume 20-25% of users leverage LLMs for decision-making.
Slower-moving industries: Start with 5-10%.

Apply these percentages to your existing SEO keyword volume. For example, a keyword with 25,000 monthly searches could translate to 1,250-6,250 LLM-based queries in your category.

Using emerging inferential tools

New platforms are beginning to track query data through API-level monitoring and machine learning models.

Accuracy isn’t perfect yet, but these tools are improving quickly. Expect major advancements in inferential LLM query modeling within the next year or two.

Get the newsletter search marketers rely on.

See terms.

Optimizing for LLM visibility

The technologies that help companies identify what to improve are evolving quickly.

While still imperfect, they’re beginning to form a framework that parallels early SEO development, where better tracking and data gradually turned intuition into science.

Optimization breaks down into two main questions:

What content should you create or update, and should you focus on quality content, entities, schema, FAQs, or something else?
How should you align these insights with broader brand and SEO strategies?

Identify what content to create or update

One of the most effective ways to assess your current position is to take a representative sample of high-intent queries that people might ask an LLM and see how your brand shows up relative to competitors. This is where the Share of Voice tracking tools we discussed earlier become invaluable.

These same tools can help answer your optimization questions:

Track who is being cited or mentioned for each query, revealing competitive positioning.
Identify which queries your competitors appear for that you don’t, highlighting content gaps.
Show which of your own queries you appear for and which specific assets are being cited, pinpointing what’s working.

From this data, several key insights emerge:

Thematic visibility gaps: By analyzing trends across many queries, you can identify where your brand underperforms in LLM responses. This paints a clear picture of areas needing attention. For example, you’re strong in SEO but not in PPC content.
Third-party resource mapping: These tools also reveal which external resources LLMs reference most frequently. This helps you build a list of high-value third-party sites that contribute to visibility, guiding outreach or brand mention strategies.
Blind spot identification: When cross-referenced with SEO performance, these insights highlight blind spots; topics or sources where your brand’s credibility and representation could improve.

Understand the overlap between SEO and LLM optimization

LLMs may be reshaping discovery, but SEO remains the foundation of digital visibility.

Across five competitive categories, brands ranking on Google’s first page appeared in ChatGPT answers 62% of the time – a clear but incomplete overlap between search and AI results.

That correlation isn’t accidental.

Many retrieval-augmented generation (RAG) systems pull data from search results and expand it with additional context.

The more often your content appears in those results, the more likely it is to be cited by LLMs.

Brands with the strongest share of voice in LLM responses are typically those that invested in SEO first.

Strong technical health, structured data, and authority signals remain the bedrock for AI visibility.

What this means for marketers:

Don’t over-focus on LLMs at the expense of SEO. AI systems still rely on clean, crawlable content and strong E-E-A-T signals.
Keep growing organic visibility through high-authority backlinks and consistent, high-quality content.
Use LLM tracking as a complementary lens to understand new research behaviors, not a replacement for SEO fundamentals.

Redefine on-page and off-page strategies for LLMs

Just as SEO has both on-page and off-page elements, LLM optimization follows the same logic – but with different tactics and priorities.

Off-page: The new link building

Most industries show a consistent pattern in the types of resources LLMs cite:

Wikipedia is a frequent reference point, making a verified presence there valuable.
Reddit often appears as a trusted source of user discussion.
Review websites and “best-of” guides are commonly used to inform LLM outputs.

Citation patterns across ChatGPT, Gemini, Perplexity, and Google’s AI Overviews show consistent trends, though each engine favors different sources.

This means that traditional link acquisition strategies, guest posts, PR placements, or brand mentions in review content will likely evolve.

Instead of chasing links anywhere, brands should increasingly target:

Pages already being cited by LLMs in their category.
Reviews or guides that evaluate their product category.
Articles where branded mentions reinforce entity associations.

The core principle holds: brands gain the most visibility by appearing in sources LLMs already trust – and identifying those sources requires consistent tracking.

On-page: What your own content reveals

The same technologies that analyze third-party mentions can also reveal which first-party assets, content on your own website, are being cited by LLMs.

This provides valuable insight into what type of content performs well in your space.

For example, these tools can identify:

What types of competitor content are being cited (case studies, FAQs, research articles, etc.).
Where your competitors show up but you don’t.
Which of your own pages exist but are not being cited.

From there, three key opportunities emerge:

Missing content: Competitors are cited because they cover topics you haven’t addressed. This represents a content gap to fill.
Underperforming content: You have relevant content, but it isn’t being referenced. Optimization – improving structure, clarity, or authority – may be needed.
Content enhancement opportunities: Some pages only require inserting specific Q&A sections or adding better-formatted information rather than full rewrites.

Leverage emerging technologies to turn insights into action

The next major evolution in LLM optimization will likely come from tools that connect insight to action.

Early solutions already use vector embeddings of your website content to compare it against LLM queries and responses. This allows you to:

Detect where your coverage is weak.
See how well your content semantically aligns with real LLM answers.
Identify where small adjustments could yield large visibility gains.

Current tools mostly generate outlines or recommendations.

The next frontier is automation – systems that turn data into actionable content aligned with business goals.

Timeline and expected results

While comprehensive LLM visibility typically builds over 6-12 months, early results can emerge faster than traditional SEO.

The advantage: LLMs can incorporate new content within days rather than waiting months for Google’s crawl and ranking cycles.

However, the fundamentals remain unchanged.

Quality content creation, securing third-party mentions, and building authority still require sustained effort and resources.

Think of LLM optimization as having a faster feedback loop than SEO, but requiring the same strategic commitment to content excellence and relationship building that has always driven digital visibility.

From SEO foundations to LLM visibility

LLM traffic remains small compared to traditional search, but it’s growing fast.

A major shift in resources would be premature, but ignoring LLMs would be shortsighted.

The smartest path is balance: maintain focus on SEO while layering in LLM strategies that address new ranking mechanisms.

Like early SEO, LLM optimization is still imperfect and experimental – but full of opportunity.

Brands that begin tracking citations, analyzing third-party mentions, and aligning SEO with LLM visibility now will gain a measurable advantage as these systems mature.

In short:

Identify the third-party sources most often cited in your niche and analyze patterns across AI engines.
Map competitor visibility for key LLM queries using tracking tools.
Audit which of your own pages are cited (or not) – high Google rankings don’t guarantee LLM inclusion.
Continue strong SEO practices while expanding into LLM tracking – the two work best as complementary layers.

Approach LLM optimization as both research and brand-building.

Don’t abandon proven SEO fundamentals. Rather, extend them to how AI systems discover, interpret, and cite information.

Read more at Read More

How to balance speed and credibility in AI-assisted content creation

October 28, 2025/0 Comments/in Search Engine Land, Web Design News/by

AI tools can help teams move faster than ever – but speed alone isn’t a strategy.

As more marketers rely on LLMs to help create and optimize content, credibility becomes the true differentiator.

And as AI systems decide which information to trust, quality signals like accuracy, expertise, and authority matter more than ever.

It’s not just what you write but how you structure it. AI-driven search rewards clear answers, strong organization, and content it can easily interpret.

This article highlights key strategies for smarter AI workflows – from governance and training to editorial oversight – so your content remains accurate, authoritative, and unmistakably human.

Create an AI usage policy

More than half of marketers are using AI for creative endeavors like content creation, IAB reports.

Still, AI policies are not always the norm.

Your organization will benefit from clear boundaries and expectations. Creating policies for AI use ensures consistency and accountability.

Only 7% of companies using genAI in marketing have a full-blown governance framework, according to SAS.

However, 63% invest in creating policies that govern how generative AI is used across the organization.

*Source- “Marketers and GenAI- Diving Into the Shallow End,” SAS*

Even a simple, one-page policy can prevent major mistakes and unify efforts across teams that may be doing things differently.

As Cathy McPhillips, chief growth officer at the Marketing Artificial Intelligence Institute, puts it:

“If one team uses ChatGPT while others work with Jasper or Writer, for instance, governance decisions can become very fragmented and challenging to manage. You’d need to keep track of who’s using which tools, what data they’re inputting, and what guidance they’ll need to follow to protect your brand’s intellectual property.”

So drafting an internal policy sets expectations for AI use in the organization (or at least the creative teams).

When creating a policy, consider the following guidelines:

What the review process for AI-created content looks like.
When and how to disclose AI involvement in content creation.
How to protect proprietary information (not uploading confidential or client information into AI tools).
Which AI tools are approved for use, and how to request access to new ones.
How to log or report problems.

Logically, the policy will evolve as the technology and regulations change.

Keep content anchored in people-first principles

It can be easy to fall into the trap of believing AI-generated content is good because it reads well.

LLMs are great at predicting the next best sentence and making it sound convincing.

But reviewing each sentence, paragraph, and the overall structure with a critical eye is absolutely necessary.

Think: Would an expert say it like that? Would you normally write like that? Does it offer the depth of human experience that it should?

“People-first content,” as Google puts it, is really just thinking about the end user and whether what you are putting into the world is adding value.

Any LLM can create mediocre content, and any marketer can publish it. And that’s the problem.

People-first content aligns with Google’s E-E-A-T framework, which outlines the characteristics of high-quality, trustworthy content.

E-E-A-T isn’t a novel idea, but it’s increasingly relevant in a world where AI systems need to determine if your content is good enough to be included in search.

According to evidence in U.S. v. Google LLC, we see quality remains central to ranking:

“RankEmbed and its later iteration RankEmbedBERT are ranking models that rely on two main sources of data: [redacted]% of 70 days of search logs plus scores generated by human raters and used by Google to measure the quality of organic search results.”

*Source: U.S. v. Google LLC court documentation*

It suggests that the same quality factors reflected in E-E-A-T likely influence how AI systems assess which pages are trustworthy enough to ground their answers.

So what does E-E-A-T look like practically when working with AI content? You can:

Review Google’s list of questions related to quality content: Keep these in mind before and after content creation.
Demonstrate firsthand experience through personal insights, examples, and practical guidance: Weave these insights into AI output to add a human touch.
Use reliable sources and data to substantiate claims: If you’re using LLMs for research, fact-check in real time to ensure the best sources.
Insert authoritative quotes either from internal stakeholders or external subject matter experts: Quoting internal folks builds brand credibility while external sources lend authority to the piece.
Create detailed author bios: Include:
- Relevant qualifications, certifications, awards, and experience.
- Links to social media, academic papers (if relevant), or other authoritative works.
Add schema markup to articles to clarify the content further: Schema can clarify content in a way that AI-powered search can better understand.
Become the go-to resource on the topic: Create a depth and breadth of material on the website that’s organized in a search-friendly, user-friendly manner. You can learn more in my article on organizing content for AI search.

*Source: Creating helpful, reliable, people-first content,” Google Search Central*

Dig deeper: Writing people-first content: A process and template

Train the LLM

LLMs are trained on vast amounts of data – but they’re not trained on your data.

Put in the work to train the LLM, and you can get better results and more efficient workflows.

Here are some ideas.

Maintain a living style guide

If you already have a corporate style guide, great – you can use that to train the model. If not, create a simple one-pager that covers things like:

Audience personas.
Voice traits that matter.
Reading level, if applicable.
The do’s and don’ts of phrases and language to use.
Formatting rules such as SEO-friendly headers, sentence length, paragraph length, bulleted list guidelines, etc.

You can refresh this as needed and use it to further train the model over time.

Build a prompt kit

Put together a packet of instructions that prompts the LLM. Here are some ideas to start with:

The style guide
- This covers everything from the audience personas to the voice style and formatting.
- If you’re training a custom GPT, you don’t need to do this every time, but it may need tweaking over time.
A content brief template
- This can be an editable document that’s filled in for each content project and includes things like:
  - The goal of the content.
  - The specific audience.
  - The style of the content (news, listicle, feature article, how-to).
  - The role (who the LLM is writing as).
  - The desired action or outcome.
Content examples
- Upload a handful of the best content examples you have to train the LLM. This can be past articles, marketing materials, transcripts from videos, and more.
- If you create a custom GPT, you’ll do this at the outset, but additional examples of content may be uploaded, depending on the topic.
Sources
- Train the model on the preferred third-party sources of information you want it to pull from, in addition to its own research.
- For example, if you want it to source certain publications in your industry, compile a list and upload it to the prompt.
- As an additional layer, prompt the model to automatically include any third-party sources after every paragraph to make fact-checking easier on the fly.
SEO prompts
- Consider building SEO into the structure of the content from the outset.
- Early observations of Google’s AI Mode suggest that clearly structured, well-sourced content is more likely to be referenced in AI-generated results.

With that in mind, you can put together a prompt checklist that includes:

Crafting a direct answer in the first one to two sentences, then expanding with context.
Covering the main question, but also potential subquestions (“fan-out” queries) that the system may generate (for example, questions related to comparisons, pros/cons, alternatives, etc.).
Chunking content into many subsections, with each subsection answering a potential fan-out query to completion.
Being an expert source of information in each individual section of the page, meaning it’s a passage that can stand on its own.
Provide clear citations and semantic richness (synonyms, related entities) throughout.

Dig deeper: Advanced AI prompt engineering strategies for SEO

Create custom GPTs or explore RAG

A custom GPT is a personalized version of ChatGPT that’s trained on your materials so it can better create in your brand voice and follow brand rules.

It mostly remembers tone and format, but that doesn’t guarantee the accuracy of output beyond what’s uploaded.

Some companies are exploring RAG (retrieval-augmented generation) to further train LLMs on the company’s own knowledge base.

RAG connects an LLM to a private knowledge base, retrieving relevant documents at query time so the model can ground its responses in approved information.

While custom GPTs are easy, no-code setups, RAG implementation is more technical – but there are companies/technologies out there that can make it easier to implement.

That’s why GPTs tend to work best for small or medium-scale projects or for non-technical teams focused on maintaining brand consistency.

RAG, on the other hand, is an option for enterprise-level content generation in industries where accuracy is critical and information changes frequently.

Run an automated self-review

Create parameters so the model can self-assess the content before further editorial review. You can create a checklist of things to prompt it.

For example:

“Is the advice helpful, original, people-first?” (Perhaps using Google’s list of questions from its helpful content guidance.)
“Is the tone and voice completely aligned with the style guide?”

Have an established editing process

Even the best AI workflow still depends on trained editors and fact-checkers. This human layer of quality assurance protects accuracy, tone, and credibility.

Editorial training

About 33% of content writers and 24% of marketing managers added AI skills to their LinkedIn profiles in 2024.

Writers and editors need to continue to upskill in the coming year, and, according to the Microsoft 2025 annual Work Trend Index, AI skilling is the top priority.

Professional training creates baseline knowledge so your team gets up to speed faster and can confidently handle outputs consistently.

This includes training on how to effectively use LLMs and how to best create and edit AI content.

In addition, training content teams on SEO helps them build best practices into prompts and drafts.

Editorial procedures

Ground your AI-assisted content creation in editorial best practices to ensure the highest quality.

This might include:

Identifying the parts of the content creation workflow that are best suited for LLM assistance.
Conducting an editorial meeting to sign off on topics and outlines.
Drafting the content.
Performing the structural edit for clarity and flow, then copyediting for grammar and punctuation.
Getting sign-off from stakeholders.

The AI editing checklist

Build a checklist to use during the review process for quality assurance. Here are some ideas to get you started:

Every claim, statistic, quote, or date is accompanied by a citation for fact-checking accuracy.
All facts are traceable to credible, approved sources.
Outdated statistics (more than two years) are replaced with fresh insights.
Draft meets the style guide’s voice guidelines and tone definitions.
Content adds valuable, expert insights rather than being vague or generic.
For thought leadership, ensure the author’s perspective is woven throughout.
Draft is run through the AI detector, aiming for a conservative percentage of 5% or less AI.
Draft aligns with brand values and meets internal publication standards.
Final draft includes explicit disclosure of AI involvement when required (client-facing/regulatory).

Grounding AI content in trust and intent

AI is transforming how we create, but it doesn’t change why we create.

Every policy, workflow, and prompt should ultimately support one mission: to deliver accurate, helpful, and human-centered content that strengthens your brand’s authority and improves your visibility in search.

Dig deeper: An AI-assisted content process that outperforms human-only copy

Read more at Read More

Structured data with schema for search and AI

October 28, 2025/0 Comments/in Web Design News, Yoast SEO/by

Structured data helps search engines, Large Language Models (LLMs), AI assistants, and other tools understand your website. Using Schema.org and JSON-LD, you make your content clearer and easier to use across platforms. This guide explains what structured data is, why it matters today, and how you can set it up the right way.

Key takeaways

Structured data helps search engines and AI better understand your website, enhancing visibility and eligibility for rich results.
Using Schema.org and JSON-LD improves content clarity and connects different pieces of information graphically.
Implementing structured data today prepares your content for future technologies and AI applications.
Yoast SEO simplifies structured data implementation by automatically generating schema for various content types.
Focus on key elements like business details and products to maximize the impact of your structured data.

What is structured data?

Structured data is a way to tell computers exactly what’s on your web page. Using a standard set of tags from Schema.org, you can identify important details, like whether a page is about a product, a review, an article, an event, or something else.

This structured format helps search engines, AI assistants, LLMs, and other tools understand your content quickly and accurately. As a result, your site may qualify for special features in search results and can be recognized more easily by digital assistants or new AI applications.

Structured data is written in code, with JSON-LD being the most common format. Adding it to your pages gives your content a better chance to be found and understood, both now and as new technologies develop.

A simple example of structured data

Below is a simple example of structured data using Schema.org in JSON-LD format. This is a basic schema for a product with review properties. This code tells search engines that the page is a product (Product). It provides the name and description of the product, pricing information, the URL, plus product ratings and reviews. This allows search engines to understand your products and present your content in search results.

<!DOCTYPE html>
<html lang="en">
<head>
    <title>Product Title</title>
    <meta name="description" content="Brief description of the product">
    <script type="application/ld+json">
    {
      "@context": "https://schema.org",
      "@type": "Product",
      "name": "Sample Product",
      "image": "https://www.example.com/product-image.jpg",
      "description": "Product description",
      "brand": {
        "@type": "Brand",
        "name": "Brand Name"
      },
      "sku": "12345",
      "offers": {
        "@type": "Offer",
        "url": "https://www.example.com/product-page",
        "priceCurrency": "USD",
        "price": "99.99",
        "availability": "https://schema.org/InStock"
      },
      "aggregateRating": {
        "@type": "AggregateRating",
        "ratingValue": "4.5",
        "reviewCount": "11"
      },
      "review": [{
        "@type": "Review",
        "reviewRating": {
          "@type": "Rating",
          "ratingValue": "4",
          "bestRating": "5"
        },
        "author": {
          "@type": "Person",
          "name": "Jane Smith"
        },
        "reviewBody": "Review text goes here"
      }]
    }
    </script>
</head>
<body>
    <!-- Your webpage content goes here -->
</body>
</html>

Why do you need structured data?

Structured data gives computers a clear map of what’s on your website. It spells out details about your products, reviews, events, and much more in a format that’s easy for search engines and other systems to process.

This clarity leads to better visibility in search, including features like star ratings, images, or additional links. But the impact reaches further now. Structured data also helps AI assistants, voice search tools, and new web platforms like chatbots powered by Large Language Models understand and represent your content with greater accuracy.

New standards, such as NLWeb (Natural Language Web) and MCP (Model Context Protocol), are emerging to help different systems share and interpret web content consistently. Adding structured data today not only gives your site an advantage in search but also prepares it for a future where your content will flow across more platforms and digital experiences.

The effort you put into structured data now sets up your content to be found, used, and displayed in many places where people search and explore online.

Is structured data important for SEO?

Structured data plays a key role in how your website appears in search results. It helps search engines understand and present your content with extra features, such as review stars, images, and additional links. These enhanced listings can catch attention and drive more clicks to your site.

While using structured data doesn’t directly increase your rankings, it does make your site eligible for these rich results. That alone can set you apart from competitors. As search engines evolve and adopt new standards, well-structured data ensures your content stays visible and accessible in the latest search features.

For SEO, structured data is about making your site stand out, improving user experience, and giving your content the best shot at being discovered, both now and as search technology changes.

Structured data can lead to rich results

By describing your site for search engines, you allow them to do exciting things with your content. Schema.org and its support are constantly developing, improving, and expanding. As structured data forms the basis for many new developments in the SEO world, there will be more shortly. Below is an overview of the rich search results available; examples are in Google’s Search Gallery.

Structured data type	Example use/description
Article	News, blog, or sports article
Breadcrumb	Navigation showing page position
Carousel	Gallery/list from one site (with Recipe, Course, Movie, Restaurant)
Course list	Lists of educational courses
Dataset	Large datasets (Google Dataset Search)
Discussion forum	User-generated forum content
Education Q&A	Education flashcard Q&As
Employer aggregate rating	Ratings about employers in job search results
Event	Concerts, festivals, and other events
FAQ	Frequently asked questions pages
Image metadata	Image creator, credit, and license details
Job posting	Listings for job openings
Local business	Business details: hours, directions, ratings
Math solver	Structured data for math problems
Movie	Lists of movies, movie details
Organization	About your company: name, logo, contact, etc.
Practice problem	Education practice problems for students
Product	Product listings with price, reviews, and more
Profile page	Info on a single person or organization
Q&A	Pages with a single question and answers
Recipe	Cooking recipes, steps, and ingredients
Review snippet	Short review/rating summaries
Software app	Ratings and details on apps or software
Speakable	Content for text-to-speech on Google Assistant
Subscription and paywalled content	Mark articles/content behind a paywall
Vacation rental	Details about vacation property listings
Video	Video info, segments, and live content

The rich results formerly known as rich snippets

You might have heard the term “rich snippets” before. Google now calls these enhancements “rich results.” Rich results are improved search listings that use structured data to show extra information, like images, reviews, product details, or FAQs, directly in search.

For example, a product page marked up with structured data can show its price, whether it’s in stock, and customer ratings right below the search listing, even before someone clicks. Here’s what that might look like:

Some listings offer extra information, like star ratings or product details

With rich results, users see helpful details up front—such as a product’s price, star ratings, or stock status. This can make your listing stand out and attract more clicks.

Keep in mind, valid structured data increases your chances of getting rich results, but display is controlled by Google’s systems and is never guaranteed.

Keep reading: Rich snippets everywhere »

Mobile rich results

Results like this often appear more prominently on mobile devices. Search listings with structured data can display key information, like product prices, ratings, recipes, or booking options, in a mobile-friendly format. Carousels, images, and quick actions are designed for tapping and swiping with your finger.

For example, searching for a recipe on your phone might bring up a swipeable carousel showing photos, cooking times, and ratings for each dish. Product searches can highlight prices, availability, and reviews right in the results, helping users make decisions faster.

Many people now use mobile search as their default search method. Well-implemented structured data not only improves your visibility on mobile but can also make your content easier for users to explore and act on from their phones. To stay visible and competitive, regularly check your markup and make sure it works smoothly on mobile devices.

Knowledge Graph Panel

The Knowledge Graph Panel shows key facts about businesses, organizations, or people beside search results on desktop and at the top on mobile. It can include your logo, business description, location, contact details, and social profiles.

Using structured data, especially Organization, LocalBusiness, or Person markup with current details, helps Google recognize and display your entity accurately. Include recommended fields like your official name, logo, social links (using sameAs), and contact info.

Entity verification is becoming more important. Claim your Knowledge Panel through Google, and make sure your information is consistent across your website, social media, and trusted directories. Major search engines and AI assistants use this entity data for results, summaries, and answers, not just in search but also in AI-powered interfaces and smart devices.

While Google decides who appears in the Knowledge Panel and what details are shown, reliable structured data, verified identity, and a clear online presence give you the best chance of being featured.

Different kinds of structured data

Schema.org includes many types of structured data. You don’t need to use them all, just focus on what matches your site’s content. For example:

If you sell products, use product schema
For restaurant or local business sites, use local business schema
Recipe sites should add recipe schema

Before adding structured data, decide which parts of your site you want to highlight. Check Google’s or other search engines’ documentation to see which types are supported and what details they require. This helps ensure you are using the markup that will actually make your content stand out in search and other platforms.

How Yoast SEO helps with structured data

Yoast SEO automatically adds structured data to your site using smart defaults, making it easier for search engines and platforms to understand your content. The plugin supports a wide range of content types, like articles, products, local businesses, and FAQs, without the need for manual schema coding.

With Yoast SEO, you can:

With a few clicks, set the right content type for each page (such as ContactPage, Product, or Article)
Use built-in WordPress blocks for FAQs and How-tos, which generate valid schema automatically
Link related entities across your site, such as authors, brands, and organizations, to help search engines see the big picture
Adjust schema details per page or post through the plugin’s settings

Yoast SEO also offers an extensible structured data platform. Developers can build on top of Yoast’s schema framework, add custom schema types, or connect other plugins. This helps advanced users or larger sites tailor their structured data for specific content, integrations, or new standards.

Yoast keeps pace with updates to structured data guidelines, so your markup stays aligned with what Google and other platforms support. This makes it easier to earn rich results and other search enhancements.

Yoast SEO helps you fine-tune your schema structured data settings per page

Which structured data types matter most?

When adding structured data, focus first on the types that have the biggest impact on visibility and features in Google Search. These forms of schema are widely supported, trigger rich results, and apply to most kinds of sites:

Most important structured data types

Article: For news sites, blogs, and sports publishers. Adding Article schema can enable rich results like Top Stories, article carousels, and visual enhancements
Product: Essential for ecommerce. Product schema helps show price, stock status, ratings, and reviews right in search. This type is key for online stores and retailers
Event: For concerts, webinars, exhibitions, or any scheduled events. Event schema can display dates, times, and locations directly in search results, making it easier for people to find and attend
Recipe: This is for food blogs and cooking sites. The recipe schema supports images, cooking times, ratings, and step-by-step instructions as rich results, giving your recipes extra prominence in search
FAQPage: For any page with frequently asked questions. This markup can expand your search listing with Q&A drop-downs, helping users get answers fast
QAPage: For online communities, forums, or support sites. QAPage schema helps surface full question-and-answer threads in search
ReviewSnippet: This markup is for feedback on products, books, businesses, or services. It can display star ratings and short excerpts, adding trust signals to your listings
LocalBusiness is vital for local shops, restaurants, and service providers. It supplies address, hours, and contact info, supporting your visibility in the map pack and Knowledge Panel
Organization: Use this to describe your brand or company with a logo, contact details, and social profiles. Organization schema feeds into Google’s Knowledge Panel and builds your online presence
Video: Mark up video content to enable video previews, structured timestamps (key moments), and improved video visibility
Breadcrumb: This feature shows your site’s structure within Google’s results, making navigation easier and your site look more reputable

Other valuable or sector-specific types:

Course: Highlight educational course listings and details for training providers or schools
JobPosting: Share open roles in job boards or company careers pages, making jobs discoverable in Google’s job search features
SoftwareApp: For software and app details, including ratings and download links
Movie: Used for movies and film listings, supporting carousels in entertainment searches and extra movie details
Dataset: Makes large sets of research or open data discoverable in Google Dataset Search
DiscussionForum: Surfaces user-generated threads in dedicated “Forums” search features
ProfilePage: Used for pages focused on an individual (author profiles, biographies) or organization
EmployerAggregateRating: Displays company ratings and reviews in job search results
PracticeProblem: For educational sites offering practice questions or test prep
VacationRental: Displays vacation property listings and details in travel results

Special or supporting types:

Person: This helps Google recognize and understand individual people for entity and Knowledge Panel purposes (it does not create a direct rich result)
Book: Can improve book search features, usually through review or product snippets
Speakable: Reserved for news sites and voice assistant features; limited support
Image metadata, Math Solver, Subscription/Paywalled content: Niche markups that help Google properly display, credit, or flag special content
Carousel: Used in combination with other types (like Recipe or Movie) to display a list or gallery format in results

When choosing which schema to add, always select types that match your site’s actual content. Refer to Google’s Search Gallery for the latest guidance and requirements for each type.

Adding the right structured data makes your pages eligible for rich results, enhances your visibility, and prepares your content for the next generation of search features and AI-powered platforms.

Read on: Local business listings with Schema.org and JSON-LD »

Structured data for voice assistants

Voice search remains important, with a significant share of online queries now coming from voice-enabled devices. Structured data helps content be understood and, in some cases, selected as an answer for voice results.

The Speakable schema (for marking up sections meant to be read aloud by voice assistants) is still officially supported, but adoption is mostly limited to news content. Google and other assistants also use a broader mix of signals, like content clarity, authority, E-E-A-T, and traditional structured data, to power their spoken answers.

If you publish news or regularly answer concise, fact-based questions, consider using Speakable markup. For other content types, focus on structured data and well-organized, user-focused pages to improve your chances of being chosen by voice assistants. Voice search and voice assistants continue to draw on featured snippets, clear Q&A, and trusted sources.

Google Search Console

If you need to check how your structured data is performing in Google, check your Search Console. Find the structured data insights under the Enhancement tab and you’ll see all the pages that have structured data, plus an overview of pages that give errors, if any. Read our Beginner’s guide for Search Console for more info.

The technical details

Structured data uses Schema.org’s hierarchy. This vocabulary starts with broad types like Thing and narrows down to specific ones, such as Product, Movie, or LocalBusiness. Every type has its own properties, and more specific types inherit from their ancestors. For example, a Movie is a type of CreativeWork, which is a type of Thing.

When adding structured data, select the most specific type that fits your content. For a movie, this means using the Movie schema. For a local company, choose the type of business that best matches your offering under LocalBusiness.

Properties

Every Schema.org type includes a range of properties. While you can add many details, focus on the properties that Google or other search engines require or recommend for rich results. For example, a LocalBusiness should include your name, address, phone number, and, if possible, details such as opening hours, geo-coordinates, website, and reviews. You’ll find our Local SEO plugin (available in Yoast SEO Premium) very helpful if you need help with your local business markup.

Here are two examples of structures:

Movie hierarchy

Thing
CreativeWork
- Movie
- Properties: name, description, director, actor, image, genre, duration

Local business hierarchy

Thing
Organization/Place
- LocalBusiness
- Properties: name, address, phone, email, openingHours, geo, review, logo

The more complete and accurate your markup, the greater your chances of being displayed with enhanced features like Knowledge Panels or map results. For details on recommended properties, always check Google’s up-to-date structured data documentation.

In the local business example, you’ll see that Google lists several required properties, like your business’s NAP (Name and Phone) details. There are also recommended properties, like URLs, geo-coordinates, opening hours, etc. Try to fill out as many of these as possible because search engines will only give you the whole presentation you want.

Structured data should be a graph

When you add structured data to your site, you’re not just identifying individual items, but you’re building a data graph. A graph in this context is a web of connections between all the different elements on your site, such as articles, authors, organizations, products, and events. Each entity is linked to others with clear relationships. For instance, an article can be marked as written by a certain author, published by your organization, and referencing a specific product. These connections help search engines and AI systems see the bigger picture of how everything on your site fits together.

Creating a fully connected data graph removes ambiguity. It allows search engines to understand exactly who created content, what brand a product belongs to, or where and when an event takes place, rather than making assumptions based on scattered information. This detailed understanding increases the chances that your site will qualify for rich results, Knowledge Panels, and other enhanced features in search. As your website grows, a well-connected graph also makes it easier to add new content or expand into new areas, since everything slots into place in a way that search engines can quickly process and understand.

Yoast SEO builds a graph

With Yoast SEO, many of the key connections are generated automatically, giving your site a solid foundation. Still, understanding the importance of building a connected data graph helps you make better decisions when structuring your own content or customizing advanced schema. A thoughtful, well-linked graph sets your site up for today’s search features, while making it more adaptable for the future.

Your schema should be a well-formed graph for easier understanding by search engines and AI

Beyond search: AI, assistants, and interoperability

Structured data isn’t just about search results. It’s a map that helps AI assistants, knowledge graphs, and cross‑platform apps understand your content. It’s not just about showing a richer listing; it’s about enabling reliable AI interpretation and reuse across contexts.

Today, the primary payoff is still better search experiences. Tomorrow, AI systems and interoperable platforms will rely on clean, well‑defined data to summarize, reason about, and reuse your content. That shift makes data quality more important than ever.

Practical steps for today

Keep your structured data clean with a few simple habits. Use the same names for people, organizations, and products every time they appear across your site. Connect related information so search engines can see the links. For example, tie each article to its author or a product to its brand. Fill in all the key details for your main schema types and make sure nothing is missing. After making changes or adding new content, run your markup through a validation tool. If you add any custom fields or special schema, write down what they do so others can follow along later. Doing quick checks now and then keeps your data accurate and ready for both search engines and AI.

Interoperability, MCP, and the role of structured data

More and more, AI systems and search tools are looking for websites that are easy to understand, not just for people but also for machines. The Model Context Protocol (MCP) is gaining ground as a way for language models like Google Gemini and ChatGPT to use the structured data already present on your website. MCP draws on formats like Schema.org and JSON-LD to help AI match up the connections between things such as products, authors, and organizations.

Another project, the Natural Language Web (NLWeb), an open project developed by Microsoft, aims to make web content easier for AI to use in conversation and summaries. NLWeb builds on concepts like MCP, but hasn’t become a standard yet. For now, most progress and adoption are happening with MCP, and large language models are focusing their efforts on this area.

Using Schema.org and JSON-LD to keep your structured data clean (no duplicate entities), complete (all indexable content included), and connected (relationships preserved) will prepare you for search engines and new AI-driven features appearing across the web.

Schema.org and JSON-LD: the foundation you can trust

Schema.org and JSON-LD remain the foundation for structured data on the web. They enable today’s rich results in search and form the basis for how AI systems will interpret web content in the future. JSON-LD should be your default format for new markup, allowing you to build structured data graphs that are clean, accurate, and easy to maintain. Focus on accuracy in your markup rather than unnecessary complexity.

To future-proof your data, prioritize stable identifiers such as @id and use clear types to reduce ambiguity. Maintain strong connections between related entities across your pages. If you develop custom extensions to your structured data, document them thoroughly so both your team and automated tools can understand their purpose.

Design your schema so that components can be added or removed without disrupting the entire graph. Make a habit of running validations and audits after you change your site’s structure or content.

Finally, stay current by following guidance and news from official sources, including updates about standards such as NLWeb and MCP, to ensure your site remains compatible with both current search features and new interoperability initiatives.

What do you need to describe for search engines?

To get the most value from structured data, focus first on the most important elements of your site. Describe the details that matter most for users and for search, such as your business information, your main products or services, reviews, events, or original articles. These core pieces of information are what search engines look for to understand your site and display enhanced results.

Rather than trying to mark up everything, start with the essentials that best match your content. As your experience grows, you can build on this foundation by adding more detail and creating links between related entities. Accurate, well-prioritized markup is both easier to maintain and more effective in helping your site stand out in search results and across new AI-driven features.

How to implement structured data

We’d like to remind you that Yoast SEO comes with an excellent structured data implementation. It’ll automatically handle most sites’ most pressing structured data needs. Of course, as mentioned below, you can extend our structured data framework as your needs become bigger.

Do the Yoast SEO configuration and get your site’s structured data set up in a few clicks! The configuration is available for all Yoast SEO users to help you get your plugin configured correctly. It’s quick, it’s easy, and doing it will pay off. Plus, if you’re using the new block editor in WordPress you can also add structured data to your FAQ pages and how-to articles using our structured data content blocks.

Thanks to JSON-LD, there’s nothing scary about adding the data to your pages anymore. This JavaScript-based data format makes it much easier to add structured data since it forms a block of code and is no longer embedded in the HTML of your page. This makes it easier to write and maintain, plus both humans and machines better understand it. If you need help implementing JSON-LD structured data, you can enroll in our free Structured Data for Beginners course, our Understanding Structured Data course, or read Google’s introduction to structured data.

Structured data with JSON-LD

JSON-LD is the recommended way to add structured data to your site. All major search engines, including Google and Bing, now fully support this format. JSON-LD is easy to implement and maintain, as it keeps your structured data separate from the main HTML.

Yoast SEO automatically creates a structured data graph for every page, connecting key elements like articles, authors, products, and organizations. This approach helps search engines and AI systems understand your site’s structure. Our developer resources include detailed Schema documentation and example graphs, making it straightforward to extend or customize your markup as your site grows.

Tools for working with structured data

Yoast SEO automatically handles much of the structured data in the background. You could extend our Schema framework, of course — see the next chapter –, but if adding code by hand seems scary, you could try some of the tools listed below. If you need help with how to proceed, ask your web developer for help. They will fix this for you in a couple of minutes.

Generators
- Google Structured Data Markup Helper (works, but hasn’t been updated in forever)
- JSON-LD Schema Generator
Validators and test tools
WordPress Plugins
- Yoast SEO Local (Our Local SEO plugin adds Schema.org for your business details, like address, geo-location, opening hours, etc.)
- The Yoast SEO WooCommerce plugin outputs product Schema to highlight your products in search.
- Yoast SEO uses JSON-LD to add Schema.org information about your site search, your site name, your logo, images, articles, social profiles, and a lot more to your web pages. We ask if your site represents a person or an organization and adapt our structured data based on that. Also, our structured data content blocks for the WordPress block editor make it easy to add structured data to your FAQs and How-Tos. Check out the structured data features in Yoast SEO.

The Yoast SEO Schema structured data framework

Implementing structured data has always been challenging. Also, the results of most of those implementations often needed improvement. At Yoast, we set out to enhance the Schema output for millions of sites. For this, we built a Schema framework, which can be adapted and extended by anyone. We combined all those loose bits and pieces of structured data that appear on many sites, improved these, and put them in a graph. By interconnecting all these bits, we offer search engines all your connections on a silver platter.

See this video for more background on the schema graph.

Of course, there’s a lot more to it. We can also extend Yoast SEO output by adding specific Schema pieces, like how-tos or FAQs. We built structured data content blocks for use in the WordPress block editor. We’ve also enabled other WordPress plugins to integrate with our structured data framework, like Easy Digital Downloads, The Events Calendar, Seriously Simple Podcasting, and WP Recipe Maker, with more to come. Together, these help you remove barriers for search engines and users, as it has always been challenging to work with structured data.

Expanding your structured data implementation

A structured and focused approach is key to successful Schema.org markup on your website. Start by understanding Schema.org and how structured data can influence your site’s presence in search and beyond. Resources like Yoast’s developer portal offer useful insights into building flexible and future-proof markup.

Always use JSON-LD as recommended by Google, Bing, and Yoast. This format is easy to maintain and works well with modern websites. To maximize your implementation, use tools and frameworks that allow you to add, customize, and connect Schema.org data efficiently. Yoast SEO’s structured data framework, for example, enables seamless schema integration and extensibility across your site.

Validate your structured data regularly with tools like the Rich Results Test or Schema Markup Validator and monitor Google Search Console’s Enhancements reports for live feedback. Reviewing your markup helps you fix issues early and spot opportunities for richer results as search guidelines change. Periodically revisiting your strategy keeps your markup accurate and effective as new types and standards emerge.

Read up

By following the guidelines and adopting a comprehensive approach, you can successfully get structured data on your pages and enhance the effectiveness of your schema.org markup implementation for a robust SEO performance. Read the Yoast SEO Schema documentation to learn how Yoast SEO works with structured data, how you can extend it via an API, and how you can integrate it into your work.

Several WordPress plugins already integrate their structured data into the Yoast SEO graph

Keep on reading: Open-source software, open Schema protocol! »

Conclusions about structured data

Structured data has become an essential part of building a visible, findable, and adaptable website. Using Schema.org and JSON-LD not only helps search engines understand your content but also sets your site up for better performance in new AI-driven features, rich results, and across platforms.

Start by focusing on the most important parts of your site, like business information, products, articles, or events, and grow your structured data as your needs evolve. Connected, well-maintained markup now prepares your site for search, AI, and whatever comes next in digital content.

Explore our documentation and training resources to learn more about best practices, advanced integrations, or how Yoast SEO can simplify structured data. Investing the time in good markup today will help your content stand out wherever people (or algorithms) find it.

The post Structured data with schema for search and AI appeared first on Yoast.

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2. Build localized visibility in AI-driven environments

3. Develop an AI-assisted content flywheel

4. Create a consistent, data-driven experience flywheel

5. Use AI agents to orchestrate journeys and workflows

6. Redefine KPIs for an AI-first performance model

7. Integrate systems and data to power a unified marketing infrastructure

The next phase of search and the customer journey

Getting started with Google Local Services Ads

Getting more from your Google Search ads

Combining LSAs and Search campaigns for stronger results

Expanding beyond LSAs and Search campaigns

Test and compare

Tracking: The foundation of LLM optimization

The polling-based model for measuring visibility

Building a multi-faceted tracking framework

Estimating LLM ‘search volume’

Optimizing for LLM visibility

Identify what content to create or update

Understand the overlap between SEO and LLM optimization

Redefine on-page and off-page strategies for LLMs

Leverage emerging technologies to turn insights into action

Timeline and expected results

From SEO foundations to LLM visibility

Create an AI usage policy

Keep content anchored in people-first principles

Train the LLM

Maintain a living style guide

Build a prompt kit

Create custom GPTs or explore RAG

Run an automated self-review

Have an established editing process

Editorial training

Editorial procedures

The AI editing checklist

Grounding AI content in trust and intent

Table of contents

Key takeaways

What is structured data?

A simple example of structured data

Why do you need structured data?

Is structured data important for SEO?

Structured data can lead to rich results

The rich results formerly known as rich snippets

Mobile rich results

Knowledge Graph Panel

Different kinds of structured data

How Yoast SEO helps with structured data

Which structured data types matter most?

Most important structured data types

Other valuable or sector-specific types:

Special or supporting types:

Structured data for voice assistants

Google Search Console

The technical details

Properties

Structured data should be a graph

Yoast SEO builds a graph

Beyond search: AI, assistants, and interoperability

Practical steps for today