AI Search
AI search is reshaping how people find information online, and how content gets discovered, cited, and surfaced when the answer itself, rather than a list of links, is the product.
What is AI search?
AI search refers to the generation of direct answers by large language models (LLMs) and AI-powered search engines, in place of (or alongside) the traditional ranked list of blue links. Systems like Google’s AI Overviews, Perplexity, ChatGPT Search, and Microsoft Copilot synthesise information from across the web and present a conversational response, often with citations.
This shift changes what it means to “rank”. Visibility in AI search is about whether your content is retrieved, parsed, trusted, and cited by the system generating the answer.
The practice of optimising for these surfaces goes by several names: Generative Engine Optimisation (GEO), Answer Engine Optimisation (AEO), LLMO, and AI SEO are all in use. They describe the same underlying goal. This pillar uses GEO as the primary term; the GEO guide covers how the terms relate.
Core AI search elements
- AI Overviews. Google’s generative answer feature, shown at the top of many results pages. Appearing as a cited source requires accurate, well-structured content that answers specific questions clearly.
- Generative Engine Optimisation (GEO). The practice of structuring content for retrieval and citation by AI answer engines. Overlaps with traditional SEO but emphasises entity clarity, source authority, and direct answer formats.
- llms.txt. A proposed convention for declaring an AI-readable index of a site’s content. Google’s AI optimisation guide confirms1 it is not necessary for search visibility; publish it only if you want to provide curated documentation for your audience.
- Markdown Pages for AI Agents. Cloudflare and others offer tools to auto-serve Markdown versions of pages to AI crawlers. No evidence supports a citation benefit; the approach is useful only for developer documentation where AI coding tools are the primary audience.
- Entity SEO. AI systems reason about entities (people, organisations, products, concepts) more than they reason about keywords. Establishing what your site is and what it covers helps LLMs cite it accurately.
- ChatGPT Search optimisation. How ChatGPT’s web search retrieval works and what content patterns it favours.
- Perplexity citation strategies. How Perplexity sources its answers and how to be one of those sources.
- Google AI Mode. Google’s conversational search tab, how it differs from AI Overviews, and what content it retrieves and cites.
- AI Content Risks. Why reckless AI content production tends to reduce search and AI citation visibility: Google’s spam policy, hallucination, E-E-A-T erosion, and the irony that volume-over-quality AI strategies undermine the signals that earn citations.
- How AI Search Works. The mechanics behind AI answer generation: training data versus real-time retrieval, grounding (RAG), why crawlability feeds into citation potential, and why new sites can appear in AI answers.
- Knowledge Graph and knowledge panels. What the Knowledge Graph is, how entities get into it, what triggers a knowledge panel, and how to influence your entity representation in search results.
- RAG and SEO. How Retrieval-Augmented Generation determines which passages AI search engines cite, and how to structure content for retrieval.
- AI Visibility Measurement. Citation rate, share of voice, and prompt coverage: the metrics that capture AI search visibility where traditional rankings do not.
- What are AI agents?. What AI agents are, how they differ from AI assistants and chatbots, and how the main categories (interactive vs background) map to search and content visibility.
- Agentic search. How AI agents retrieve content across multi-step research tasks, and what makes content comprehensive enough to reduce agent multi-hopping.
- Agentic SEO. How AI agents execute SEO workflows autonomously: keyword research, content briefs, technical audits, and performance reporting. What tasks agents handle reliably, where human oversight remains essential, and what orchestration tools are available.
- Gemini Spark. Google’s personal AI agent: how it monitors topics and compiles digests from web content, and what it means for publishers whose content is synthesised without a click.
- WebMCP. The browser-native protocol that lets websites declare callable tools for AI agents. Where agentic search optimises content for retrieval, WebMCP declares what agents can do on a site. Early priority for e-commerce, travel, and booking sites.
- NLWeb. Microsoft’s open-source protocol that converts existing schema.org markup into queryable AI interfaces, exposing
/ask(REST) and/mcp(MCP server) endpoints. Built by R.V. Guha (creator of Schema.org, RSS, and RDF). Most relevant for commerce, media, and documentation sites with deep content catalogues; informational sites should monitor rather than implement. - Query Fan-Out. How AI Mode expands a single query into multiple parallel sub-queries, and why topical coverage across a full cluster matters more than matching a single keyword.
- AI crawler user-agents. A reference to active AI crawlers, who operates them, whether they are used for training or live retrieval, and how to verify legitimacy. Training crawlers and retrieval crawlers are separate systems: blocking one does not affect the other.
- Claude Search optimisation. How Claude retrieves web content via Brave Search, which of Anthropic’s three crawlers affects citation visibility, and what content patterns earn citations in Claude’s responses.
- Microsoft Copilot optimisation. How Copilot retrieves content from the Bing index, what content patterns improve citation likelihood, and how to track Copilot citations via Bing Webmaster Tools’ AI Performance report.
- Agent-readiness. The infrastructure layer that determines whether AI agents can discover, read, and act on a website. Covers the five infrastructure layers, what Cloudflare, Shopify, and WordPress each check for, and what content sites should implement now.
Why does AI search matter?
The share of searches that return an AI-generated answer is growing rapidly. For informational queries in particular, AI Overviews are becoming the dominant surface. Sites that aren’t optimised for generative retrieval will see organic traffic decline even if their traditional rankings hold.
AI search also introduces zero-click risk at scale. If an AI summarises your content and provides the answer directly, users may never visit your site. Citation and brand visibility in AI responses is a new form of organic reach that matters independently of click-through rate.
AI search and E-E-A-T
AI systems are trained to favour content that exhibits genuine expertise, clear authorship, and factual accuracy. The same E-E-A-T principles that influence Google’s quality raters also shape what AI engines choose to cite. First-hand experience, named authors, cited sources, and content that goes beyond surface-level coverage all improve the chances of being surfaced in AI-generated answers.
The brands and publications that dominate AI citations over the next few years will be those that invested in genuine authority, not those that optimised purely for keyword placement.
Understanding how AI search works, from retrieval mechanisms to entity recognition, is the foundation for these investments. AI content risks, including hallucination and E-E-A-T erosion from unreviewed AI-generated production, are real penalties that reduce citation potential. For sites that serve transactional queries, agent readiness, the infrastructure layer that lets AI agents discover and act on a site, is the next consideration beyond content quality alone.