BE DISCOVERABLE BY AI
robots.txt, sitemaps, canonicals, indexability and AI crawler access form the discovery foundation.
A technique that retrieves relevant external information and uses it to ground a generated response.
Google's 2026 generative AI Search guide explicitly describes retrieval-augmented generation (RAG), also called grounding, as a technique used with Search ranking systems to retrieve relevant up-to-date pages before generating responses.
Retrieval works better when important information is textual, coherent, uniquely useful, linked and accessible at stable URLs.
The site owner wants useful facts, products and expertise to be retrievable in the right context when an AI system answers a relevant question.
HTML&HTML evaluates oversized content blocks, context loss, duplication, self-contained sections, discovery paths and semantic integrity.
RAG is a retrieval technique, not a standalone Google ranking factor and not a guarantee that a particular page will be cited.
HTML&HTML prepares your website for visibility, citation eligibility and recommendation opportunity across AI search experiences. It shows measurable website-side blockers that can prevent discovery, understanding and source consideration.
robots.txt, sitemaps, canonicals, indexability and AI crawler access form the discovery foundation.
GEO, AEO, LLMO, entity graphs, schema and answer extractability reduce machine ambiguity.
RAG/retrieval, original information, E-E-A-T, freshness and evidence support source eligibility.
AAO, accessible journeys, intact links, measurable referrals and clear CTAs connect AI discovery to commercial action.
Recommendations, rankings, citations, traffic, customers and revenue are not guaranteed. HTML&HTML measures website-side technical and content blockers; it does not claim control over external AI systems.