From search to answer engines: How to optimize for the next era of discovery

The shift from traditional search engines to AI-powered answer engines signals more than a technical upgrade.

It marks a fundamental change in how people discover, evaluate, and act on information. 

Search is no longer a discrete game of isolated queries and static rankings. 

It’s becoming an infinite game – one shaped by context, memory, and ongoing interaction. 

For many users, large language models (LLMs) now offer a more effective starting point than classic search engines, especially when the task calls for clarity, research, or a more conversational experience.

How search evolved: From static queries to continuous conversations

Traditional search: A one-off query model

Traditional search engines (like classic Google Search) operate on a deterministic ranking model. 

Content is parsed, analyzed, and displayed in SERPs largely as provided. 

Ranking depends on known factors:

• Content quality.
• Site architecture.
• Links.
• User signals. 

A user types a query, receives a list of results (“10 blue links”), clicks, and typically ends the interaction. 

Each query is treated independently, with no memory between sessions. 

This model supports advertising revenue by creating monetization opportunities for every new query.

AI-powered search: Built for continuity and context

AI-powered answer engines use a probabilistic ranking model. 

They synthesize and display information by incorporating:

• Reasoning steps.
• Memory of prior interactions.
• Dynamic data. 

The same query can yield different results at different times. 

These systems are built for ongoing, multiturn conversations, anticipating follow-up questions and refining answers in real time. 

They operate continuously, even while you sleep, and focus on delivering direct, synthesized answers rather than just pointing to links.

How output and experience differ between search and answer engines

The differences between traditional search and AI-powered answer engines aren’t just technical. They show up in what users see and how they interact. 

From output format to underlying signals, the user experience has fundamentally changed.

From link lists to zero-click answers

• Traditional search engines: Return a ranked list of links generated by complex algorithms.
• Answer engines: Deliver full answers, summaries, direct responses, or even product recommendations by blending large-scale training data with real-time web results. They reduce the need for users to click through multiple sites, leading to more zero-click experiences.

From keywords to context

• Traditional search: Relies on keyword matching, backlinks, and on-page optimization.
• AI search/generative engines: Rely on semantic clarity, contextual understanding, and relationships between entities enhanced by attention mechanisms and references in credible sources. Even content that doesn’t rank highly in traditional search may appear prominently in AI summaries if it is well-structured, topical, and cited across trusted platforms. 

Key characteristics of answer engines

Conversational search

LLMs like ChatGPT, Google Gemini, and Perplexity enable conversational interactions, often serving as a more intuitive starting point for users seeking clarity, context, or nuanced understanding. 

Queries tend to be longer and phrased as full questions or instructions.

Personalization and memory

Unlike traditional search, AI-powered search incorporates user context, such as:

• Past queries.
• Preferences.
• Location.
• Even data from connected ecosystems (e.g., Gmail within Google’s AI Mode). 

This context allows the engine to deliver tailored, dynamic, and unique answers.

Dig deeper: How to boost your marketing revenue with personalization, connectivity and data

Query fan-out

Instead of processing a single query, answer engines deconstruct a user’s question into dozens or even hundreds of related, implicit, comparative, and personalized sub-queries. 

These synthetic queries explore a broader content pool. 

From one query, systems like AI Mode or AI Overviews:

• Generate a constellation of search intents.
• Retrieve responsive documents.
• Build a custom corpus of relevant content. 

Reasoning chains

AI models move beyond keyword matching, performing multi-step logical reasoning. They: 

• Interpret intent.
• Formulate intermediate steps.
• Synthesize coherent answers from multiple sources.

Multimodality

Answer engines can process information in various formats, including text, images, videos, audio, and structured data. They can:

• Transcribe videos.
• Extract claims from podcasts.
• Interpret diagrams.
• Integrate these inputs into synthesized outputs.

Dig deeper: Visual content and SEO: How to use images and videos in 2025

Chunk-level retrieval

Instead of retrieving or ranking entire pages, AI engines work at the passage level. 

They extract and rank smaller, highly relevant chunks of content to build precise, context-rich answers.

Advanced processing features

User embeddings and personalization

• Systems like Google’s AI Mode use vector-based profiles that represent each user’s history, preferences, and behavior. 
• This influences how queries are interpreted and how content is selected, synthesized and surfaced as a result – different users may receive different answers to the same query.

Deep reasoning

• LLMs evaluate relationships between concepts, apply context, and weigh alternatives to generate responses. 
• Content is judged on how well it supports inference and problem-solving, not just keyword presence.

Pairwise ranking prompting

• Candidate passages are compared directly against each other by the model to determine which is most relevant, precise, and complete. 
• This approach departs from traditional scoring models by favoring the best small sections rather than entire documents

A step-by-step guide to answer-engine-optimized content

Content best practices remain the same – it should be people-centric, helpful, entity-rich with healthy topical coverage based on audience intent.

However, the content creation process needs to incorporate answer-engine optimization best practices in the details.

Here’s our recommended seven-step process for content creation.

1. Content audit

When auditing existing content:

• Check current visibility signals, including impressions, rich results, and whether the page is cited in AI platforms like Google AI Overviews, ChatGPT, or Perplexity.
• Identify signs of content decay to establish a baseline for measuring improvement.
• Spot and document issues such as:
  • Topical gaps or missing subtopics.
  • Unanswered user questions.
  • Thin or shallow content sections.
  • Outdated facts, broken references, or weak formatting.
  • Grammatical errors, duplicate content, or poor page structure.

2. Content strategy

It is not all about creating new content. 

Your content strategy should incorporate aligning existing content to the needs of answer engines.

• Retain: High-converting content with high visibility and high traffic.
• Enhance: Pages with high impressions but low click-through rate, pages with low visibility, impressions, and rich results.
• Create: Content around topical gaps found in the audit.

3. Content refresh

Update existing content to close topical gaps to make information easily retrievable

4. Content chunking

This involves breaking long blocks into:

• Scannable sections (H2/H3).
• Bullet lists.
• Tables,
• A short TL;DR/FAQs. 

Keep each chunk self-contained so LLMs can quote it without losing context, and cover just one idea per chunk.

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

5. Content enrichment

Fill in topical gaps by:

• Expanding on related topics.
• Adding fresh data.
• Drawing on first-hand examples.
• Referencing expert quotes.

Cover topics AI can’t easily synthesize on its own. 

Cite and link to primary sources within the text (where relevant and meaningful) to boost credibility.

6. Layer on machine-readable signals

Insert or update schema markup (FAQPage, HowTo, Product, Article, etc.). 

Use clear alt text and file names to describe images.

7. Publish → monitor → iterate

After publishing, track organic visibility, AI citation frequency, and user engagement and conversion. 

Schedule content check-ins every 6–12 months (or after major core/AI updates) to keep facts, links, and schema current. 

Make your content LLM-ready: A practical checklist

Below is a checklist you could incorporate in your process to ensure your content aligns with what LLMs and answer engines are looking for.

Map topics to query fan-out

• Build topic clusters with pillar and cluster pages.
• Cover related questions, intents, and sub-queries.
• Ensure each section answers a specific question.

Optimize for assage-level retrieval

• Use clear H2/H3 headings phrased as questions.
• Break content into short paragraphs and bullet points.
• Include tables, lists, and visuals with context.

Build depth and breadth

• Cover topics comprehensively (definitions, FAQs, comparisons, use cases).
• Anticipate follow-up questions and adjacent intents.

Personalize for diverse audiences

• Write for multiple personas (beginner to expert).
• Localize with region-specific details and schema.
• Include multimodal elements (images w/ alt text, video transcripts, data tables).

Strengthen semantic and entity signals

• Add schema markup (FAQPage, HowTo, Product).
• Build external mentions and links from reputable sources.
• Use clear relationships between concepts.

Show E-E-A-T and originality

• Include author bios, credentials, and expertise.
• Add proprietary data, case studies, and unique insights.

Ensure technical accessibility

• Clean HTML, fast load times, AI-friendly crawling (robots.txt).
• Maintain sitemap hygiene and internal linking.

Align with AI KPIs

• Track citations, brand mentions, and AIV (attributed influence value).
• Monitor engagement signals (scroll depth, time on page).
• Refresh content regularly for accuracy and relevance.

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How SEO is evolving into GEO

As the mechanics of search evolve, so must our strategies. 

GEO (generative engine optimization) builds on SEO’s foundations but adapts them for an environment where visibility depends on citations, context, and reasoning – not just rankings.

Many “new” AI search optimization tactics, such as focusing on conversational long-tail searches, multimodal content, digital PR, and clear content optimization, are essentially updated versions of long-standing SEO practices.

New metrics and goals 

Traditional SEO metrics like rankings and traffic are becoming less relevant. 

The focus shifts to being cited or mentioned in AI-generated answers, which becomes a key visibility event and a brand lift moment, rather than just driving traffic. 

New KPIs at the top of the funnel include:

• Search visibility.
• Rich results.
• Impressions.
• LLM visibility. 

With declining traffic, engagement, and conversion metrics become critical at the bottom of the funnel.

Relevance engineering

This emerging discipline involves:

• Strategically engineering content at the passage level for semantic similarity.
• Anticipating synthetic queries.
• Optimizing for “embedding alignment” and “informational utility” to ensure the AI’s reasoning systems select your content. 

Your website acts as a data hub. 

This also means centralizing all types of data for consistency and vectorizing data for easy consumption, and distributing it across all channels is a critical step. 

Importance of structured data

Implementing schema markup and structured data is crucial for GEO. 

It helps AI engines understand content context, entities, and relationships, making it more likely for content to be accurately extracted and cited in AI responses (53% more likely).

Dig deeper: How to deploy advanced schema at scale

Brand authority and trust

AI models prioritize information from credible, authoritative, and trustworthy sources. 

Building a strong brand presence across diverse platforms and earning reputable mentions (digital PR) is vital for AI search visibility, as LLMs may draw from forums, social media, and Q&A sites.

Connecting the dots: UX and omnichannel in the age of AI search

The typical user journey is no longer linear. The options for discovery have diversified with AI acting as a disruptor. 

Most platforms are answering questions, are multimodal, delivering agentic and personalized experiences. 

Your audience expects similar experiences on the sites they visit. As the user journey evolves, our approach to marketing needs to change, too. 

In a linear journey, having channel-based strategies worked. 

Consistency of messaging, content, visuals and experiences at every touchpoint are today key to success. 

That means you need an audience strategy before mapping channels to the strategy.

Dig deeper: Integrating SEO into omnichannel marketing for seamless engagement

To make it happen effectively, you need to orchestrate the entire content experience – and that starts with your platform as the foundation.

Your website today needs to act as the data hub feeding multimodal information across channels.

How to make your content discoverable by LLMs

To show up in LLM-driven search experiences, your content needs more than depth. It needs structure, speed, and clarity. 

Here’s how to make your site visible and machine-readable.

Foundational SEO

The fundamentals of SEO still apply. 

LLMs have to crawl and index your content, so technical SEO elements like crawlability and indexability matter. 

LLMs do not have the crawl budgets or computing power that Google and Bing have. 

That makes speed and page experience critical to maximize crawling and indexing by LLMs

Digital assets

With search going multimodal, your digital assets – images and videos – matter more than they ever did. 

Optimize your digital assets for visual search and make sure your page structure and elements include FAQs, comparisons, definitions, and use cases.

Structural integrity 

Your site and content need to be both human and machine-readable. 

Having high-quality, unique content that addresses the audience’s needs is no longer enough. 

You need to mark it up with an advanced nested schema to make it machine-readable.

Deep topical coverage

Ensure your content aligns with the best practices of Google’s E-E-A-T.

People-first content that:

• Is unique.
• Demonstrates expertise.
• Is authoritative.
• Covers the topics that your audience cares about. 

Make your content easy to find – and easy to use

While the building blocks of SEO are still relevant, aligning with LLM search calls for refining the finer points of your marketing strategy to put your audience before the channels. 

Start with the basics and ensure your platform is set up to let you centralize, optimize and distribute content. 

Adopt IndexNow to push your content to LLMs instead of waiting for them – with their limited computing and crawling capabilities – to crawl and find your content.

Thank you, Tushar Prabhu, for helping me pull this together.

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