How AI search works: RAG, grounding and information retrieval

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.

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.

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.

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.

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.

Allow their search crawlers, put a clear, sourced answer at the top of each key page, and earn mentions on sites the engines already trust. Then track a fixed set of buyer prompts every month to see what moved.

Engines that retrieve live pages, like Perplexity, can pick up new content within weeks. Steady visibility usually takes three to six months. For an aerospace manufacturer, our work earned 74,808 Google AI impressions in three months.

Organic AI answers can’t be bought, and nobody can guarantee a citation, because each engine picks sources on its own every time. You can make your pages the clearest and best-supported answer, which raises the odds.

Separate search crawlers from training crawlers. Blocking OAI-SearchBot or PerplexityBot keeps you out of those engines’ answers. Training crawlers such as GPTBot and ClaudeBot are a separate business decision.