SMX Now: Find the entity gaps holding back your content strategy

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Your Schema Says One Thing. Google's NLP Sees Another. That Gap Is Your Content Strategy

Your schema tells search engines what your brand is. But that doesn’t mean Google’s systems understand it the same way.

That gap can reveal where your content strategy is falling short.

Our next SMX Now on Sept. 16 at 1 p.m. ET will feature Ray Martinez, VP of SEO at Archer Education. He’ll show you how to measure the difference between the entities you define and the ones Google’s natural language processing (NLP) actually recognizes.

You’ll learn how to build an entity audit using schema.org markup, the Google Cloud Natural Language API, and an agentic coding tool such as Antigravity, Claude Code, or Codex. The process turns your existing schema into a queryable knowledge graph, then compares it with competitor content to uncover topics they cover, gaps they miss, and entities Google doesn’t yet connect with your brand.

Martinez will also show you how to turn those findings into action. You’ll learn how to create content around under-recognized entities, strengthen those connections with structured data and internal data, and make your pages more retrievable and citable across search and AI engines.

You’ll leave with a repeatable way to measure discoverability, find content opportunities, and track whether Google and AI systems are getting better at understanding what your brand actually does.

Save your spot

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Google Ads AI Dashboards start appearing in advertiser accounts

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Google Ads’ new AI-powered Dashboards are rolling out to some accounts, letting advertisers create visual performance reports with simple text prompts.

Driving the news. AI Dashboards started appearing in some Google Ads accounts following Google’s announcement of the feature in August. You can use simple text prompts to turn account data into visual reports, Google said.

  • Instead of manually building a report to investigate a performance change, you can describe what you want to analyze and have Google generate the visualization.

Why we care. Building reports in Google Ads has traditionally required you to manually select metrics, dimensions, and visualizations. AI Dashboards shift much of that work to Gemini, letting you describe what you want to analyze while Google builds the report.

The details. The feature goes beyond building charts.

Each report also includes a real-time AI summary that explains the “why” behind the data, according to Google. That lets you use the dashboard to understand what changed in campaign performance and what may be driving those changes, rather than simply reviewing the underlying numbers.

The big picture. Google is increasingly putting AI between advertisers and their campaign data. It has also introduced AI-powered insights on the Google Ads homepage and Ask Advisor, its in-product AI agent, as it shifts toward workflows where advertisers can ask performance questions in natural language instead of manually navigating reports.

Bottom line. AI Dashboards reduce the work of building Google Ads reports, letting you spend more time interpreting insights rather than creating them.

First spotted. This update was first spotted by paid search expert Thomas Eccel, who shared it on LinkedIn.

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Microsoft Advertising removes Max CPC from new standalone bidding campaigns

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Microsoft Advertising will stop allowing advertisers to set Max CPC limits on new campaigns using several standalone automated bidding strategies starting Oct. 1, as the platform pushes advertisers toward conversion-based targets and other automated bidding controls.

Existing campaigns with Max CPC limits won’t immediately lose them, while portfolio bid strategies and several other bidding strategies will retain the option.

Why we care. This removes another manual control from Microsoft’s automated bidding strategies. Advertisers that use Max CPC as a safeguard against unexpectedly expensive clicks will have less direct control when creating certain new campaigns and will instead need to rely more heavily on budgets and conversion-based targets.

Whether that produces better results will depend on the quality of an advertiser’s conversion data, the targets they set and how effectively Microsoft’s bidding system responds to those signals.

What’s changing. Starting Oct. 1st, advertisers creating new campaigns using standalone Maximize Conversions, Maximize Conversion Value and Maximize Clicks bidding strategies will no longer be able to add a Max CPC.

Existing campaigns created before the deadline will retain their Max CPC settings. Microsoft Advertising Product Liaison Navah Hopkins also confirmed that Target Impression Share, eCPC and portfolio bidding strategies will continue to support Max CPC controls.

Why Microsoft is making the change. Microsoft says Max CPC limits can interfere with automated bidding by overriding an advertiser’s stated performance goals and potentially creating spend pacing irregularities.

The company says advertisers using conversion-based bidding with target CPA (tCPA) and target ROAS (tROAS) tend to have an easier time achieving their goals than advertisers relying on legacy controls such as Max CPC.

Instead of CPC caps, Microsoft wants advertisers to communicate their objectives through controls more closely tied to business outcomes, including budgets, tCPA, tROAS, conversion value rules and seasonality adjustments.

What happens to existing campaigns. There’s no immediate migration for campaigns already using Max CPC. Campaigns created before Oct. 1 will continue to retain the bidding control.

That creates an important distinction between existing and newly created campaigns: advertisers won’t necessarily lose Max CPC across their accounts on Oct. 1, but their ability to use it when building certain new campaigns will disappear.

Microsoft says further updates about the future of Max CPC will be provided later.

Get ahead of the change. Hopkins is encouraging advertisers to use optimization experiments to test removing Max CPC from existing campaigns before the deadline.

Doing so could give advertisers an indication of how campaigns perform when automated bidding has greater freedom, before the option disappears from newly created standalone campaigns.

That may be particularly relevant heading into the holiday season, when advertisers could otherwise encounter the new restriction while launching or restructuring campaigns.

Targets become the primary lever. Microsoft is encouraging advertisers to think of tCPA and tROAS as their primary volume and value levers, rather than trying to control automated bidding through individual CPC limits.

Hopkins also noted that Microsoft Advertising can allow campaigns to outperform their tCPA or tROAS targets regardless of whether they’re limited by budget. Where appropriate, Microsoft recommends using conversion value rules to provide the bidding algorithm with additional information about which conversions are more valuable to the business.

Bottom line. Microsoft Advertising is making automated bidding more target-driven by phasing Max CPC out of new standalone Maximize Conversions, Maximize Conversion Value and Maximize Clicks campaigns from Oct. 1 — while, for now, preserving the control for existing campaigns and portfolio strategies.

September 7th comms. Microsoft Advertising emailed advertisers with more information about this update.

Microsoft’s Max CPC restriction will also apply to Target CPA and Target ROAS campaigns, expanding the bid strategies beyond the Maximize Conversions, Maximize Conversion Value and Maximize Clicks strategies reported in August.

Microsoft also revealed a January 12th deadline for API users, tool providers and Google Import, after which Max CPC won’t be supported for new campaigns or existing campaigns not already using it. While existing campaigns using Max CPC by Oct. 1 can keep the setting, Microsoft now says removing it after that date is irreversible — advertisers won’t be able to add a Max CPC back later.

Dig deeper. Updates to Max CPC for new campaigns

See exactly how your competitors win.

Uncover the keywords, ads, landing pages, and strategies driving your competitors’ paid search success—and find your next opportunity to outperform them.

Analyze your competitors

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Google Ads adds new tools to drive and measure in-store sales

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Google is rolling out two new features designed to help multi-location retailers, restaurants and local service businesses reach nearby customers and connect their ad campaigns with in-store sales.

Driving the news. Google Ads Liaison Ginny Marvin announced Local Customer Optimization for Performance Max store goals campaigns and a new way to bring store sales data into Google Ads through Data Manager.

Both are aimed at reducing the work required to drive and measure offline sales ahead of the holiday rush.

Why we care. These updates make it easier to connect digital advertising with actual store visits and sales. Multi-location businesses can prioritize spend toward nearby, in-market customers across Maps, Waze and local Search, while simpler CRM and Google Sheets connections make it easier for advertisers to feed offline sales data back into Google Ads for measurement and optimization.

Zoom in. Local Customer Optimization is a new campaign-level toggle in Performance Max for store goals campaigns.

When enabled, Google will prioritize budget toward reaching consumers who are actively in-market nearby across Google Maps, Waze and local Search.

The feature is rolling out now.

Meanwhile. Google is also simplifying how businesses can share their offline sales data with Google Ads.

With Store Sales in Data Manager, advertisers will be able to connect their CRM or Google Sheets directly within Data Manager rather than relying on more technically demanding methods of sharing that data.

Advertisers can then use the information to measure in-store sales and optimize campaigns toward their highest-potential walk-in customers.

Google says the feature will begin rolling out in the coming weeks.

The big picture. The two updates are designed to work across opposite ends of the customer journey.

Local Customer Optimization uses real-time navigation and local intent signals to help businesses reach potential customers nearby, while Store Sales in Data Manager gives advertisers a simpler way to feed the resulting offline revenue data back into Google Ads.

For businesses with physical locations, that creates a more direct connection between finding nearby customers online and measuring what happens when they walk through the door.

What to watch: Local Customer Optimization is rolling out now, while Store Sales in Data Manager is expected in the coming weeks.

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How AI Shopping Works

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How AI Shopping Works: A Guide for Marketing and eCommerce Leaders An AI shopping agent is software that interprets a […]

The post How AI Shopping Works appeared first on Onely.

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Inside Google Maps: 72 ranking signals and the architecture behind local search

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Inside Google Maps- 72 ranking signals and the architecture behind local search

When a business appears in Google Maps, the listing you see is the end product of a much larger system.

Behind it sits a canonical geographic entity assembled from multiple data sources, connected to the Knowledge Graph and the web, scored by several ranking systems, filtered through geographic and semantic retrieval, personalized for the user, and finally passed to a rendering engine that decides what can actually appear on the map.

Our team recently obtained a binary exposing a non-public scope of Geostore, the system Google uses to represent geographic entities. We crossed it with Maps protocols, network traffic, the web index, mobile services, style tables, on-device components, and Google’s 2024 leak.

The recovered material includes:

  • 72 Geostore ranking signals.
  • 793 data source providers.
  • 446 local search intent types.
  • 50,998 Mapcore styles.
  • 12,936 label styles.
  • 10,936 searchable Geostore declarations.

The ranking signals will probably attract attention. But they’re only one layer.

The architecture around them tells a more important story about how Google understands places and what local SEO may become as Maps turns into a conversational product.

The first thing to understand: A listing isn’t the entity

A useful mental model starts with Geostore. Google represents geographic objects internally as Features. A Feature can be a business, building, road, city, station, area, transit element, or even a 3D object.

For an establishment, the object can contain identity, geometry, source information, websites, business-chain relationships, Knowledge Graph references, concepts, and ranking information.

The familiar Maps listing is assembled later. What a business owner edits in Google Business Profile isn’t necessarily what Google maintains internally as the entity.

Google builds a canonical representation of the place that can incorporate data from multiple sources, survive changes in geometry, and connect to other Google identifiers, including the Knowledge Graph machine ID (MID).

For local SEO, the entity is the more useful unit to consider. The listing is the interface. The entity sits underneath it.

Turn Google searches into more calls, visits, and sales.

Everything you need to manage your GBP, dominate Google Maps, and attract more customers.

Connect your business

Google combines data from 793 providers

One of the most revealing parts of Geostore is its provenance system. 

A business doesn’t simply have one source. Its name might come from one provider, its phone number from another, its category from another, and its geometry from somewhere else entirely.

The corpus exposes 793 source providers, along with mechanisms for provenance, priority, trust, and conflation.

Conflation is the process used when several sources describe the same object and disagree.

Geostore contains generic mechanisms that can pick one value, merge several values, or combine them. It also models trust levels ranging from blocked or untrusted sources to trusted and super-trusted ones.

This gives a different interpretation to a common local SEO problem: changing a field in Google Business Profile doesn’t guarantee that Google’s canonical representation immediately becomes that new value.

The edit becomes another piece of evidence entering a system that may already have competing evidence.

For businesses struggling with persistent incorrect attributes, duplicate information, or changes that repeatedly revert, this architecture helps explain why the problem can be harder than editing a listing.

Dig deeper: The local SEO gatekeeper: How Google defines your entity

The archive makes the leak much more useful

The binary itself gives us structures, field numbers, and complete enumerations. Google’s March 2024 documentation often gives us something different: prose explaining what those structures mean.

We published the two together. The resulting archive contains 10,936 Geostore declarations that can be searched by message name, package, field type, documentation text, tag number, status, and other properties.

In reverse-engineering work, an internal name can easily become a theory once it circulates through the SEO community. The archive lets you inspect the underlying evidence.

If a signal exists, you can find its declaration. If a field existed in the 2024 documentation, you can read the associated description. If a field has been stripped from the newer client scope, its protobuf tag still leaves a numbered hole.

The 2024 leak gave us many descriptions of Google’s systems. The newer binary gives us much more of their actual vocabulary.

Together, they provide a more useful picture than either source does on its own.

Oyster Rank contains 72 ranking signals

Geostore has its own ranking system. Internally, it’s called Oyster Rank. We recovered a complete visible enumeration of 72 signals, including:

  • Google reviews.
  • Web query volume.
  • Listing impressions.
  • Listing opens.
  • Direction requests.
  • Website clicks.
  • Chain membership.
  • Wikipedia signals.
  • Popularity.
  • Prominence.
  • Landmark information.
  • Road usage.

Out of 72 values, 25 are explicitly marked deprecated.

The important limitation is that we recovered the signal names, not their current weights.

The schema shows a pipeline in which raw observations are extracted, normalized, and mixed into the Feature’s rank. But the coefficients that would tell us how much each signal contributes are outside the scope we recovered.

So SIGNAL_GOOGLE_REVIEWS proves that reviews belong to the Oyster Rank vocabulary. It doesn’t prove that reviews currently carry a particular weight in a Maps search.

The 72 signals aren’t the Google Maps algorithm

This is probably the most important clarification for SEOs. Oyster Rank appears to characterize the importance of the entity inside Geostore. A user query still has to go through additional systems.

Maps must understand what the person means, identify a geographic context, generate candidates, evaluate semantic relevance, and serve a final result set.

A simplified pipeline looks more like:

Geostore entity -> query understanding -> semantic matching -> candidate generation -> geography and quality -> reranking -> results

There are additional complications. We also found a separate scorer running entirely offline on the device. It has eight signals across 13 tiers and is distinct from both Oyster Rank and server-side Places ranking.

There isn’t a single Maps ranking formula. Different scoring and retrieval systems operate at different stages.

Turning the 72 Oyster Rank signals into a checklist of 72 Google Maps ranking factors would miss most of the architecture.

Local search doesn’t have a fixed radius

We also tested the geographic layer directly. A common local SEO model imagines Google looking within a predefined radius around the user and ranking the businesses found inside it.

Our measurements show something more dynamic. Using the same origin in Paris, the geographic footprint changed considerably depending on the query.

A dense query, such as pharmacie produced a far smaller search area than a brand query, such as Carrefour.

The environment matters, too. The same pharmacy query expanded dramatically when run in a sparsely populated rural area.

Google appears to adapt the candidate space to both the query and what exists around the user.

We then removed geographic weighting from the same engine. Across 5,083 calls and 86,584 results, the median distance moved from 6.87 km with geography to more than 4,000 km without it.

More interestingly, the non-geographic order remained extremely stable.

This suggests geography is doing more than reordering the same list of candidates by distance. It changes what the retrieval system considers in the first place.

Distance is still fundamental in local SEO. But “I’m closer, I should rank higher” is an incomplete model.

Dig deeper: The proximity paradox: Beating local SEO’s distance bias

Maps and the web are connected through entities

The connection between Maps and classic web SEO may be one of the most consequential findings in the corpus.

Geostore Features can connect to the Knowledge Graph through a MID. On the web index side, documents can also carry MIDs.

Google has a layer called webref that associates documents with entities and stores information, including topicality, confidence, geographic metadata, and document-level scores.

The relationship also works at the document-ranking level. The recovered structures describe a relative ranking signal between different documents for the same entity, along with properties such as whether a page is an author page, publisher page, or reference page.

This creates a very different way of thinking about a store locator or location page. Its role may extend beyond ranking for queries such as “shoe shop Paris.” The document can become evidence about the underlying entity.

The SEO objective is then partly to make it easy for Google to establish:

  • Which entity the document describes.
  • How much of the document is actually about that entity.
  • How confident that association should be.
  • Whether the document is a useful reference for it.

Web SEO and local SEO are much less separate inside Google’s infrastructure than their interfaces suggest.

Google understands concepts, not just categories

The semantic layer goes considerably beyond the primary category visible on a listing. Google uses GConcepts, a shared conceptual vocabulary that can describe businesses, dishes, attributes, cuisines, service modes, and other concepts.

We followed a simple ramen query through several parts of the system. The search results themselves didn’t all belong to one category. Google connected the query with ramen restaurants, Japanese restaurants, Asian restaurants, and other related concepts.

Inside listings, the semantic representation goes deeper. Review topics, menu dishes, and other attributes can be represented as entities rather than plain strings.

For an AI system, this is extremely useful. Instead of rereading thousands of reviews every time someone asks whether a restaurant has long waits or good ramen, Google can work from structured themes, entities, and precomputed signals already attached to the place.

Semantic understanding becomes much more important when the interface starts answering complex questions.

Part of Google’s geographic intelligence lives on the phone

Not everything is calculated on Google’s servers. We found on-device structures associated with visits, place candidates, frequent places, trips, home and work, mobility patterns, and user location profiles.

One particularly interesting object is ChainAffinity, which suggests the system can model affinity toward a recurring retail chain.

There’s also the separate offline scorer mentioned earlier.

The exact evidence level differs between components. Some structures are explicitly named in the recovered schema, while parts of the persona layer can only be reconstructed from compiled structures.

But the broader architecture is clear: The phone itself participates in building geographic context.

That means personalization in Maps can combine server-side knowledge of the world with a local model of the user’s own geography.

Ranking still doesn’t guarantee map visibility

Search results are only one of the outputs of Maps. 

The visual map has another problem to solve: Thousands of potentially relevant entities cannot all receive labels simultaneously. That job belongs partly to Mapcore.

We recovered 50,998 Mapcore styles and 12,936 label styles. Label visibility can change with zoom and other rendering conditions.

A business can be eligible or highly ranked and still fail to appear as a visible name on the map. Search ranking and map visibility are separate optimization problems.

This distinction becomes especially important when people measure “Maps visibility” using screenshots or map grids. The visual surface includes a rendering decision after retrieval and ranking have already happened.

Then Google puts Gemini on top

The timing of the recovery is useful. Google is rapidly expanding Ask Maps and other AI-powered experiences, but much of the infrastructure required to answer complex questions was already present.

The system already has:

  • Canonical place entities.
  • Semantic concepts and attributes.
  • Reviews and extracted topics.
  • Knowledge Graph relationships.
  • Web evidence.
  • Geographic retrieval.
  • Behavioral signals.
  • Personal geographic context.
  • Listing composition.
  • Ranking systems.

Gemini adds a conversational interface over these layers. That changes what a local query can be.

“Best ramen near me” is relatively easy. “Where can six people eat near my hotel tonight, with one vegetarian, little waiting time, and good recent feedback about service?” requires a different kind of place representation.

Google needs to know what the restaurant is, what it serves, when it’s open, what people say about it, where it is, how it relates to the user’s route or context, and whether the available evidence is reliable enough to recommend it.

Maps has been building many of those ingredients for years. The AI layer gives Google a new way to use them.

Dig deeper: Google Ask Maps: How to optimize for visibility

What to optimize beyond the Business Profile

The most actionable conclusion from this research isn’t a new list of ranking factors.

Local SEO has traditionally concentrated heavily on optimizing the Google Business Profile: categories, reviews, photos, attributes, opening hours, and other listing fields.

Those remain important.

But Google’s architecture suggests a broader objective: improve the representation Google can build of the entity itself.

For a business or retail brand, I would increasingly ask:

  • What exactly is this place?
  • What does it offer?
  • Which brand or chain does it belong to?
  • Which concepts and attributes describe it?
  • Does its website describe the same entity clearly?
  • Which Web documents provide evidence about it?
  • Does Google see real demand for the brand?
  • What do reviews consistently say about specific aspects of the experience?
  • Which audiences and contexts could make this place relevant?
  • When should Google recommend it rather than another candidate?

The quality of the answer Google can produce depends on the completeness of that representation.

Semantic completeness may become the next local SEO battleground

Proximity, relevance, and prominence remain useful concepts.

AI adds another requirement. The system needs enough structured evidence to reason about a place.

A restaurant can have an optimized profile and hundreds of reviews, yet still be poorly represented for a specific question if Google can’t confidently connect the relevant attributes, concepts, web pages, and review themes to the entity.

A large retail brand has an additional problem.

Google models both chains and individual locations. We observed stores from the same brand in the same metropolitan area carrying different primary concepts, even though the chain model contains a canonical concept structure.

Consistency can’t be assumed simply because every location belongs to the same brand.

For multi-location SEO, the task extends across the brand entity, each local entity, the website, structured data, third-party sources, user-generated content, and the relationships between all of them.

That’s a much larger surface than a Business Profile.

Dig deeper: Multi-location SEO: How to structure geographic pages at scale

Get found by more local customers.

Boost your visibility, earn more reviews, climb higher on Maps, and stay ahead of local competitors.

Start winning locally

The listing is only the surface

The 72 Oyster Rank signals are fascinating because they expose categories of information Google can use when estimating the importance of a place.

The deeper finding is the system around them:

  • Geostore builds a canonical geographic entity from competing sources.
  • The Knowledge Graph gives that entity semantic meaning.
  • Webref connects Web documents to it.
  • Search and Places interpret the query and retrieve candidates within a dynamic geographic space.
  • On-device systems contribute personal geographic context.
  • Mapcore controls what reaches the visual map.
  • Ask Maps and Gemini can finally reason over the resulting representation in natural language.

The Google Business Profile still matters. But Google’s architecture suggests looking beyond the listing itself and considering the representation Google has built of the business.

The key question is whether that representation is complete enough for Google to confidently recommend the business.

The full study includes the reconstructed architecture, experiments, and technical evidence. The companion archive exposes the 10,936 recovered Geostore declarations alongside the 2024 documentation so the underlying schemas, enums, fields, and signals can be inspected directly.

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How AI Shopping Agents Find Products

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AI shopping agents like ChatGPT and Microsoft Copilot find products by interpreting a shopper’s natural-language request, retrieving candidates from machine-readable […]

The post How AI Shopping Agents Find Products appeared first on Onely.

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Winning With Shopify podcast: Everything you need to get started on SEO with Shopify

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Winning With Shopify podcast: Everything you need to get started on SEO with Shopify



Took place on

Hosts & Guests

Alex Moss

Principal SEO

Nick Trueman

Winning With Shopify Podcast Host

Shopify SEO is changing, and ecommerce brands need to rethink how they make their stores visible, understandable, and recommendable by AI.

In this episode of Winning With Shopify, Alex Moss explores how AI search, LLMs, and agentic shopping are reshaping the way customers discover and buy products online.

From site structure and content optimization to brand mentions, reviews, citations, and structured product information, Alex shares practical ways Shopify brands can prepare for the future of AI-powered discovery.

The episode also explores the rise of agentic commerce and a future where AI agents could recommend and even purchase products on behalf of customers.

Tune in to discover how to make your Shopify store easier for both people and AI to find, understand, and recommend.

First upcoming events

SEO for beginners webinar
15 September 2026

Hosts

John Dale

Support engineer

Michael Tiña

Support engineer

SEO for beginners webinar

Learn the essentials to start SEO confidently and boost your site’s visibility.

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Reykjavik Internet Marketing Conference 2026
September 17, 2026

Who will be there:

Alex

  • Speaking

Reykjavik Internet Marketing Conference 2026

Team Yoast is Speaking at Reykjavik Internet Marketing Conference 2026! Click through…



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The SEO Update by Yoast – September 2026
5 October 2026

Hosts

Alex Moss

Principal SEO

Carolyn Shelby

Principal SEO

The SEO Update by Yoast – September 2026

Expert analysis of the latest SEO & AI news developments with Carolyn…

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Web Design and Development San Diego

Search Central Live is coming to Bogota and Ciudad de México

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Leer en español

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Prompts to programming: Vibe coding vs. traditional coding a website

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Good vibes can spark great ideas, and now they can even help you build a website. With vibe coding, you don’t need to start by writing lines of code. Instead, you tell AI what you want to create, refine the results through conversation, and watch your website take shape. While it won’t replace every aspect of traditional development, it’s opening the door for more people to bring their ideas online.

In this guide, we’ll explore what vibe coding is, how to build a website with it, and how it compares to traditional coding.

Table of contents

What’s the vibe around vibe coding?

The term vibe coding was coined in February 2025 by computer scientist Andrej Karpathy, co-founder of OpenAI and former AI leader at Tesla. He used it to describe a new way of building software: instead of writing code line by line, you simply explain what you want to create and let AI generate the technical implementation. In Karpathy’s words, you “say stuff, run stuff, and copy-paste stuff, and it mostly works.“

At its core, vibe coding is about shifting your role. Instead of acting as the developer who writes every line of code, you become the creative director. You describe your vision, review what the AI generates, ask for changes, and continue refining the result until it matches what you have in mind. Rather than focusing on syntax, frameworks, or debugging, your attention stays on the bigger picture: what you want to build and how it should work.

Overall, if I had to describe vibe coding in one sentence, “I’d say it feels less like programming and more like collaborating with an AI that speaks both your language and the language of code.“

What can you build with vibe coding?

Vibe coding isn’t limited to generating snippets of code. Today’s AI-powered tools can help you create everything from simple landing pages to complete websites, web applications, dashboards, and internal business tools!

what can you achieve with vibe coding

With vibe coding, you can:

  • Build websites and landing pages faster. AI can generate layouts, content, and page structures from a simple prompt, helping you move from idea to first draft in minutes.
  • Start with ideas instead of code. Rather than thinking about HTML, CSS, or JavaScript, you focus on your goals, audience, and the experience you want to create.
  • Generate a complete website foundation. AI can create pages, navigation menus, sections, and design elements that you can build on.
  • Focus on creativity rather than the technical setup. Spend more time shaping your messaging, branding, and design while AI handles much of the repetitive work and offloads technical tasks.
  • Customize and iterate quickly. AI-generated websites can be refined through multiple iterations, allowing you to adjust layouts, content, and other design elements until they match your needs.
  • Explore different ways to present the same content. AI can quickly generate different design approaches for the same information, making it easier to compare options and find the one that works best for your audience.

    For example, you could ask AI to create three versions of the same interface: one with low-contrast colors, one with vibrant colors, and one with a dark-mode option.

Understanding vibe coding for building a website

When the concepts of vibe coding are applied to website design, it means you can converse with an AI platform and bring your vision to life online without worrying about complex concepts like HTML, CSS, JavaScript, or other programming languages. Simply explain your brand in plain English and let AI handle much of the technical work.

For example, you could prompt an AI with something like:

“Create a modern website for a local coffee shop with warm colors, an online menu, customer testimonials, and a contact form.”

This conversational approach makes website creation much more accessible, especially for entrepreneurs, freelancers, creators, and small business owners who have ideas but little or no coding experience.

That said, vibe coding doesn’t eliminate the need for human input. AI can generate a strong starting point, but creating a successful website still depends on having a clear goal, understanding your audience, and reviewing the output before going online.

Key benefits of vibe coding a website

Vibe coding is changing the way people approach website creation. Instead of spending weeks learning programming languages or waiting for a first draft, you can start with an idea and let AI help you bring it to life.

For anyone with a website idea, that shift means more time to focus on what they want to achieve and less time spent worrying about the technical details. Even experienced developers are embracing vibe coding for rapid prototyping and repetitive development tasks because it helps them move from concept to execution much faster.

Here are some of the biggest reasons why vibe coding is gaining momentum:

Launch ideas in hours, not weeks

Every website starts as an idea, but turning that idea into something people can visit has traditionally taken time. Vibe coding shortens that journey considerably. By describing your website in natural language, AI can generate a functional first draft in minutes. Instead of staring at a blank screen, you’re reviewing something tangible and deciding how to improve it. That faster start means you can validate ideas, collect feedback, and get your business online sooner.

Also read: New: Yoast AI Content Planner turns a blank post into a structured draft

Experiment and prototypes faster

One of the biggest advantages of vibe coding is how quickly it lets you test and refine ideas. Instead of spending days or weeks building a prototype from scratch, you can describe your vision in plain language and let AI generate a working first draft in minutes.

Whether you’re exploring different layouts, validating a business idea, or creating a website for a client, you can quickly experiment, gather feedback, and improve the result through conversation, making it easier to iterate before investing significant time or resources.

Iterate through conversations

Perhaps the biggest shift isn’t that AI writes code, it’s that building a website becomes a conversation.

Instead of manually editing every element, you can ask AI to redesign a section, rewrite your homepage copy, add a testimonials section, or make the layout feel more modern. Each prompt builds on the previous one, allowing your website to evolve naturally over time.

Can you really build a website using prompts?

The answer is yes, but it’s not because AI has replaced websites with something entirely new. Instead, it has changed how we build them.

Think of prompts as the new interface for website creation. Instead of dragging elements onto a page or writing HTML and CSS, you describe your business, goals, and the kind of website you want. Modern AI website builders then translate those instructions into a functional first draft, which you can continue refining through conversation.

Also read: Perfect prompts: 10 tips for AI-driven SEO content creation

That doesn’t mean prompting replaces creativity or strategy. AI can generate the website, but it still relies on your direction. The clearer your vision and the more context you provide, the closer the final website will be to what you imagined.

How to vibe code a website in 5 steps

Building a website with vibe coding isn’t about finding the perfect prompt; it’s about following the right process. The more clearly you communicate your goals and refine the results, the closer your website will be to what you originally envisioned.

Here’s a simple five-step framework to help you get started.

1. Define the purpose and style of your website

Before you open an AI website builder, spend a few minutes thinking about what you want to build. The better you define your vision, the better AI can translate it into a website.

Start by answering a few simple questions:

  • Who is the website for?
  • What do you want visitors to do?
  • How should your brand feel?
  • What pages or features do you need?

For example, instead of writing: “Build me a business website.”

Try something more descriptive:

“Create a modern website for a boutique coffee roastery. Use warm earth tones, clean typography, and large product images. Include a homepage, About page, online shop, customer reviews, and a contact form. The overall style should feel welcoming, premium, and easy to navigate.”

It explains the website’s purpose, functionality, and personality, which is important for launching as a brand or business that people are searching for.

Also read: What is search intent and why is it important for SEO?

2. Choose an AI website builder

Once you have a clear idea of the website you want to create, the next step is choosing the right AI website builder.

AI tools can support website development in different ways. For example, coding agents such as Claude Code offer specialized capabilities for building websites, but they typically require you to work in a development environment or via a command-line interface.

Claude Code terminal works in a development environment

On the other hand, AI website builders offer a more accessible alternative: instead of working in a terminal or configuring a development environment, you can describe what you want through a conversational interface and start with a functional website that you can refine.

If you’re building a website with AI, the Bluehost AI Website Builder is a great place to start. It asks a few questions about your business, goals, and preferred style before generating a personalized website that you can continue to customize. Because it’s built into Bluehost’s hosting platform, you can go from idea to a live website without juggling multiple tools or worrying about technical setup.

From there, your role shifts from building pages manually to guiding AI toward the website you envision.

3. Write your first vibe prompt

Your first prompt lays the foundation for everything that follows, so it’s worth spending a little extra time getting it right.

This is where the idea of context engineering becomes useful. Rather than trying to cram every instruction into one clever prompt, think about the information AI needs to understand your project: what you’re building, who it’s for, how it should look and feel, what pages it needs, and what you want visitors to do.

You can build this context around:

  • your business or project
  • your target audience
  • your preferred design style
  • the pages you need
  • important features or functionality
  • your brand’s tone of voice

The key idea is simple: don’t focus on writing a clever prompt; focus on giving AI the context it needs to make good decisions. And as you continue vibe coding, you can add or refine that context through subsequent conversations rather than trying to get everything right in your first message.

4. Refine your website through conversation

Don’t expect your first prompt to produce the perfect website, and that’s completely normal. Vibe coding is an iterative process. Once AI generates the first version, continue the conversation by making small, focused improvements.

For example, you could ask AI to:

  • make the hero section more visually engaging
  • rewrite your homepage copy in a friendlier tone
  • replace placeholder images with a cleaner layout
  • add customer testimonials
  • improve the mobile experience
  • make the call-to-action button more prominent

Making one change at a time usually produces better results than asking AI to redesign the entire website in a single prompt. Think of each prompt as another design review. With every iteration, your website moves closer to the experience you originally imagined.

5. Review, publish, and keep improving

Before publishing your website, take some time to review everything AI has generated.

Check that the content accurately represents your business, all links and forms work correctly, and the design looks good on both desktop and mobile devices. This is also the perfect time to replace placeholder text, add your own images, and make sure the website reflects your brand.

Once you’re happy with the result, connect your custom domain and publish your website.

One of the biggest advantages of vibe coding is that the process doesn’t stop after launch. Need to add a new service, update your homepage, or create a landing page for a campaign? Simply return to the AI website builder, describe what you want, and continue refining your website through conversation instead of starting from scratch.

How to avoid the vibe coding doom loop

vibe coding doom loop representation

One of the biggest advantages of vibe coding is how quickly you can turn an idea into a working website. But as you continue refining your prompts, there’s a chance you’ll run into what’s commonly known as the vibe coding doom loop.

This happens when AI gets stuck trying to solve the same issue repeatedly without actually fixing it. It may suggest several “solutions,” but each one either introduces a new problem or brings you back to where you started. Instead of moving your website forward, you end up going in circles.

The doom loop usually happens when AI loses track of the bigger picture. This can occur if your original prompts are too vague, you keep changing your website’s direction midway through the process, or you request too many unrelated changes at once. As the conversation becomes more complex, the AI may struggle to connect all the pieces together, leading to inconsistent results.

How to fix this situation

The good news is that the vibe coding doom loop is usually avoidable. A few simple habits can help you keep AI on track and make the website-building process much smoother.

Get clarity on what you want

As mentioned earlier, context engineering is about giving AI the information it needs to make better decisions, rather than relying on increasingly elaborate prompts. Before asking AI to make changes, consider whether you’ve provided enough context about what you’re trying to achieve, who the website is for, and what you want the change to accomplish.

For instance, if you’re refining your homepage, explain the outcome you’re aiming for and provide relevant context about your audience, messaging, layout, or user experience. This gives AI a better basis for making the change and reduces the chances of it solving one problem while creating another.

Manage AI mistakes instead of chasing them

It’s important to remember that AI isn’t perfect. Treat each suggestion as a draft rather than a final answer. Review every change before moving on, make one improvement at a time, and don’t hesitate to undo or rephrase a prompt if the output isn’t what you expected. A little oversight goes a long way in preventing small issues from turning into bigger ones.

Vibe coding vs. traditional coding: What’s the difference?

By now, you’ve probably noticed that vibe coding doesn’t completely reinvent website development; it simply changes the way you interact with it. Instead of writing every line of code yourself, you’re guiding AI with prompts and refining the results through conversation.

That doesn’t mean traditional coding has become obsolete. Developers still rely on it to build highly customized websites, complex web applications, and features that require complete control over every aspect of the codebase. Vibe coding, on the other hand, focuses on speed, accessibility, and helping people bring ideas to life without getting bogged down in technical details.

Ultimately, both approaches can produce excellent websites. The biggest difference isn’t the final outcome; it’s the path you take to get there.

Website aspect Traditional coding Vibe coding
Primary input Programming languages like HTML, CSS, JavaScript, and PHP Natural language prompts
Required skills Coding and web development knowledge Clear communication and prompt writing
Development process Manual coding and debugging AI-assisted generation and refinement
Time to first draft Usually longer Usually much faster
Customization Unlimited control High, depending on the AI tool
Best suited for Complex, highly customized websites and applications Business websites, portfolios, landing pages, MVPs, and rapid prototyping
Barrier to entry Higher Lower, making it accessible to non-coders

Vibe coding a website: FAQs

Who should vibe code a website?

Vibe coding is a great fit for entrepreneurs, freelancers, creators, marketers, and small business owners who want to launch a professional website without having to learn to code. It’s also useful for designers and developers who want to prototype ideas quickly before investing time in a full-scale build. If you have a clear vision but limited technical experience, vibe coding can help you bring that vision online much faster.

Which tools are best for website vibe coding?

If you’re building a website, an AI website builder is the easiest place to start because it combines AI-generated design, hosting, and customization in one workflow. For example, the Bluehost AI Website Builder helps turn your prompts into a personalized website that you can continue to refine through conversation. If you’re creating an online store, Bluehost AI Store provides AI-powered ecommerce capabilities, while the Bluehost AI All Access Pack brings together multiple AI tools for building, managing, and growing your website.

Once your website is live, you can complement it with Yoast SEO AI+. It helps you prepare for the future of search by monitoring how your brand appears in AI-powered search experiences, tracking AI visibility over time, and providing insights into your brand’s presence alongside competitors.

How long does it take to create a website with an AI website builder?

The answer depends on your website’s complexity, but one of the biggest advantages of vibe coding is how quickly you can create a functional first draft. Many AI website builders can generate a website within minutes, while the remaining time is usually spent refining the design, content, and functionality to match your goals.

If you’d like a detailed breakdown of what influences the timeline, read our guide on how long it takes to build a website with AI.

Your prompts build the site. SEO helps people find it

Creating a website is an exciting milestone, but publishing it is only the beginning. Whether you’ve built your website through traditional development or vibe coding, it still needs to be discovered by the people you’re trying to reach. That’s where SEO comes in.

Today, your website isn’t just being read by search engines. AI assistants and other intelligent systems are also interpreting your content to answer questions, recommend businesses, and surface relevant information. That means your website needs to communicate clearly with both people and machines.

If you’re running a WordPress website, Yoast SEO Premium helps lay that foundation. It automatically generates structured data that helps search engines and AI systems better understand your pages, your content, and your expertise. Features such as schema aggregation create a unified view of your website’s structured data, while the Bot Blocker gives you more control over which AI systems can use your content for training.

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SEO doesn’t stop once your website is published, either. Creating optimized titles and meta descriptions for every page can be time-consuming, especially as your website grows. Yoast SEO Premium helps speed up that process with AI-powered suggestions that can draft SEO titles and meta descriptions for individual pages or in bulk. Every suggestion remains fully editable, giving you the final say before anything goes live.

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