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In practice
The proposal’s logic: models struggle to parse sprawling sites, so a curated file — the site’s key pages with concise descriptions — could serve as a table of contents for machines. The scepticism’s evidence: no major AI system has documented using llms.txt for retrieval or answers, the file sits outside the standards that demonstrably work (robots.txt for access, sitemaps for discovery, structured data for extraction), and the engines that comment on it describe sitemaps and clean HTML as the effective inputs.
The honest posture is therefore experimental and cheap: maintaining an llms.txt costs little (it is a curated index you should have anyway, in machine-readable form), it does nothing harmful, and if any engine adopts it, early adopters are ready — but no programme should claim it as a lever, budget around it, or let it substitute for the confirmed work: crawl access, extractable pages, entity clarity, placement on cited sources. The useful reframing: the exercise of writing it — choosing the site’s key pages and describing each in one honest sentence — is itself a content-architecture audit that pays regardless of the file’s fate.
The evaluation rule for buyers: when a vendor lists llms.txt as a GEO deliverable, ask what else is in the package — if the confirmed layers (access, extraction, entity, placement, measurement) are there too, the file is a harmless bonus; if it’s the headline, the programme is decoration.
See also: LLM optimisation, GEO checklist for SaaS sites, AI crawler access.
Related service: Brand mentions.
