How AI search works: RAG, grounding and information retrieval
AI answers are not written from memory alone. Most AI search systems look up live pages, keep the passages that best answer the question, and cite them. Here is how that works, drawn from the platforms’ own documentation and the research behind it.
What the data says
What retrieval-augmented generation (RAG) and grounding are
Retrieval-augmented generation was introduced in a 2020 paper by Patrick Lewis and colleagues, accepted at NeurIPS 2020. Their RAG models paired a pre-trained language model, which the paper calls parametric memory, with a dense vector index of Wikipedia searched by a neural retriever, the non-parametric memory. The authors reported more specific and factual output than a model answering from its training alone, and noted that language models on their own struggle to show where an answer came from or to update what they know. An external index helps with both.
Grounding is the same idea applied in live products. Google’s Search Central documentation describes RAG, which it says is “also known as grounding,” as using Google’s core Search systems to retrieve relevant, up-to-date pages from the Search index to improve the quality, accuracy and freshness of AI responses. Google’s Gemini API documentation shows the flow: the model decides whether a search would improve the answer, generates and runs one or more queries, synthesizes the results, and returns the answer with citations tied to specific passages of text. Microsoft documents a similar pattern for Copilot, which uses Bing results to give a more grounded response.
How AI search retrieves, ranks and cites sources
Most AI search starts by breaking the question apart. Google says AI Overviews and AI Mode may use “query fan-out,” issuing multiple related searches across subtopics and data sources, which is why they can link to a wider set of pages than a classic results page. Microsoft says Copilot turns the prompt into a short query of a few words, sends it to Bing, and shows the exact queries in the citation section of its answer. Each query then retrieves candidates from an index, and access matters: OpenAI says sites that opt out of OAI-SearchBot will not be shown in ChatGPT search answers, and Perplexity says PerplexityBot is what surfaces and links websites in its search results.
Retrieval usually casts a wide net, then narrows it. The major platforms do not publish their full reranking logic, but Anthropic’s published retrieval research shows the standard pattern: pull a large candidate set (the top 150 chunks in its tests), score each one against the query with a reranking model, and pass only the best 20 to the language model. The model writes from the passages it kept and attaches citations. Google says its systems can understand multiple topics on a page and surface the relevant piece, and that supporting links come from pages that are indexed and eligible for a snippet. Our guides to query fan-out and agentic RAG go deeper on each stage.
How to earn visibility on How AI search works
Let AI search crawlers in
Check robots.txt, CDN and firewall rules for Googlebot, Bingbot, OAI-SearchBot, Claude-SearchBot and PerplexityBot. OpenAI says sites that block OAI-SearchBot are not shown in ChatGPT search answers, and Anthropic says blocking Claude-SearchBot may reduce visibility. Training crawlers such as GPTBot and ClaudeBot are a separate decision.
Make your entity unambiguous
Describe who you are, what you make and who you serve the same way on your site, profiles and listings. Add Organization, Product and FAQ structured data that matches visible text. Google says it is not required for AI search but helps it understand pages, and Bing advises reducing ambiguity across text, images and video.
Write answer-first passages
Open each section with a direct answer, then the proof: specs, tolerances, prices, steps. Bing recommends clear headings, tables and FAQ sections. Google says you do not need to chop content into tiny pieces, because its systems can find the relevant part of a page, so write for buyers first.
Earn corroboration across sources
Fan-out pulls from many sites at once, so being described accurately on industry publications, distributor and partner pages, review sites and communities gives retrieval more places to find you. Google warns that chasing inauthentic mentions is not as helpful as it might seem, so earn real coverage.
Keep key pages fresh
Google describes grounding as a way to improve the freshness of AI answers, and Bing recommends keeping content current and notifying it of changes through IndexNow. Date and update the pages buyers rely on: specifications, pricing guidance, comparisons, certifications and lead times.
Publish original first-party data
Google’s guidance favors unique, non-commodity content with a distinct point of view, and Bing advises backing claims with examples and data. Test results, benchmarks, application notes and buyer research that only you hold give AI systems a reason to cite you rather than a rewrite of you.
It is not just about traffic
Citations are a means, not the goal. A buyer who asks an AI assistant for a shortlist and sees your brand cited arrives further along, and industry studies from Similarweb and Semrush find AI-referred visitors convert 2–5x higher than classic organic traffic. AlchemyLeads is a revenue-first AI search agency: we map the sub-queries your buyers’ prompts fan out into, fix what keeps your pages out of retrieval, build the evidence that gets them chosen, and measure citations against MQLs, SQLs and sales pipeline in your CRM. That approach helped an industrial ground-monitoring manufacturer earn 4,500 AI citations across 18 pages in six months and 170% more requests for quote.
Straight answers
What is grounding in AI search?
Grounding means anchoring an AI answer in sources retrieved at the time of the question rather than relying only on what the model learned in training. Google’s Search Central documentation treats it as another name for retrieval-augmented generation: its systems retrieve relevant, up-to-date pages from the Search index to improve the quality, accuracy and freshness of AI responses, then link to those pages as sources.
What is the difference between RAG and fine-tuning?
RAG gives a model access to outside information at answer time, such as web pages or a document index. Fine-tuning retrains a model on examples so it performs a task more consistently, for example in a fixed format or tone. OpenAI describes RAG as giving the model domain-specific context and fine-tuning as improving accuracy and efficiency on a specific task, and says the two are additive. AI search visibility depends on RAG.
Does schema markup help with AI search?
Not directly, according to Google. Its generative AI guidance says structured data is not required and no special schema.org markup is needed for AI Overviews or AI Mode, but recommends keeping it because it supports rich result eligibility and helps Google understand page content. We treat schema as entity hygiene: accurate, matched to visible text, and never a substitute for clear, well-supported content.
How does Google AI Overviews choose its sources?
Google says AI Overviews and AI Mode may use query fan-out, running related searches across subtopics, and identify supporting pages while generating the answer. To be eligible, a page must be indexed and eligible to show in Google Search with a snippet. Google says there are no extra technical requirements, and that unique, useful, crawlable content is what most likely influences presence in its AI features.
Do I need an llms.txt file to appear in AI answers?
Google says Search does not use llms.txt and that it neither helps nor harms visibility. But Google's own Lighthouse added an Agentic Browsing audit in version 13.3 (May 2026) that checks for a well-formed llms.txt, and it is rolling into Chrome DevTools and PageSpeed Insights. We listen to what Google says and watch what Google builds, so we keep one in place, alongside open access for AI search crawlers.
Go deeper
Sources
- arXiv, Lewis et al., Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, May 2020
- Google Search Central, Optimizing for generative AI features on Google Search, Jul 2026
- Google Search Central, AI features and your website, Dec 2025
- Google Search Central, Introduction to structured data markup, Dec 2025
- Google AI for Developers, Grounding with Google Search, Oct 2026
- Microsoft Learn, Data, privacy, and security for web search in Microsoft Copilot and Microsoft Copilot Chat, Aug 2026
- Bing Webmaster Blog, Introducing AI Performance in Bing Webmaster Tools, Feb 2026
- OpenAI, Overview of OpenAI crawlers, Oct 2026
- OpenAI, Optimizing LLM accuracy, Oct 2026
- Anthropic, Does Anthropic crawl data from the web, Oct 2026
- Anthropic, Introducing Contextual Retrieval, Sep 2024
- Perplexity, Perplexity crawlers, Oct 2026
- Google Research Blog, Bypassing inference bottlenecks: accelerating complex AI search with Retrieve-for-Train, Sep 2026
- DebugBear, Google Lighthouse has a new Agentic Browsing category, May 2026
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