From a ranked list to a generated answer
AI search optimisation is the work of making an organisation and its expertise easier for answer engines to retrieve, interpret, trust and cite. It applies to generated experiences including Google AI Overviews and AI Mode, ChatGPT search, Perplexity and other assistants that synthesize information from several sources.
The labels vary. GEO usually means generative engine optimisation and AEO means answer engine optimisation. Both describe a change in the search result, but the foundations remain closely connected to SEO. A page must still be accessible, understandable and supported by enough evidence to deserve inclusion.
There is no single AI-search switch
Google states that its existing SEO best practices remain relevant to AI features and that no special AI schema or machine-readable file is required. OpenAI separately provides an OAI-SearchBot control for publishers who want their pages to be eligible for ChatGPT search results. These are useful platform facts, but neither creates visibility by itself.
LLMs.txt can orient systems and developers towards preferred resources, particularly on a complex site. It should be treated as a supplementary index of useful context. It cannot compensate for thin pages, blocked crawling, inconsistent facts or an absence of independent evidence.
- Allow the search crawlers you want to reach the site
- Keep important facts in indexable page text
- Connect organisation, people, services and articles consistently
- Use structured data that matches visible content
- Earn relevant third-party mentions, references and reviews
What answer-ready content looks like
Answer-ready content resolves a specific information need clearly enough that a useful passage can stand on its own. It defines specialist terms, states assumptions, explains limits and links the answer to supporting evidence. The page still needs narrative and judgement, but important facts should not be hidden behind slogans or implied through design.
Original experience is especially valuable. A service provider can explain the decisions it makes, the trade-offs it sees, common failure patterns and the evidence it uses. Those details give readers a reason to trust the page and give other publishers a reason to reference it.
How to measure progress
AI answers vary by prompt, user, location, platform and time. Measurement therefore needs a repeatable sample rather than a single screenshot. A useful baseline records a controlled set of commercial and informational prompts, whether the brand appears, how it is described, which sources are cited and where important competitors have stronger evidence.
Prompt observations should sit beside referral traffic, organic search data, conversions, crawl evidence and content performance. The purpose of measurement is to find gaps and guide work, not to turn a probabilistic answer into a fictional fixed ranking.