LLMs.txt has generated lots of heat—and little light. Here’s a pragmatic, evidence‑based take: what it is, what it isn’t, and how to use it (if at all) without burning dev hours.
TL;DR
- Major AI search experiences do not rely on LLMs.txt today. Focus on answer‑centric content, clean structure, and technical hygiene.
- It can be useful as a curated “starting map” for agent and RAG workflows.
- If you publish one, keep it minimal, accurate, non‑sensitive, and aligned with your sitemap. Treat it as optional housekeeping.
What LLMs.txt tries to do
LLMs.txt is a plain‑text file at your web root that lists high‑value pages (e.g., docs, FAQs, benchmarks) and offers light hints (update cadence, canonical locations). It is closer to a curated sitemap than a robots.txt directive—informational, not enforceable.
Current state: crawled, mostly ignored by AI search
- Public guidance for AI Overviews: follow normal SEO best practices; LLMs.txt isn’t used for inclusion or ranking.
- Large‑scale crawler logs show occasional fetches of
/llms.txtbut no meaningful effect on inclusion, ranking, or citation in AI answers. - Industry commentary broadly aligns: tiny effort, tiny impact—don’t expect visibility gains.
Where LLMs.txt can help (today)
- Agent‑first browsing: Give autonomous/semiautonomous agents a curated jump‑off list for definitive resources (docs, changelogs, pricing, comparisons).
- RAG seed lists: Provide initial URLs for chunking/indexing in retrieval pipelines.
- Enterprise assistants: Standardize “where to start” across microsites and subdomains.
Risks and limitations
- Voluntary and unenforceable: Providers aren’t obligated to honor it.
- Redundant: Good pages are already discoverable via HTML, sitemaps, and links.
- Misconfiguration risk: Don’t surface sensitive, ephemeral, or low‑quality URLs.
Minimal LLMs.txt (quick start)
version: 1
urls:
- https://example.com/docs/
- https://example.com/pricing/
- https://example.com/blog/
notes: canonical resources; updated monthly
Guidelines:
- Keep it short and consistent with your public sitemap.
- Only list pages you would confidently recommend to an agent or researcher.
- Add a brief notes line for cadence or canonicals; avoid proprietary data.
Adoption checklist (do no harm)
- Confirm there’s no expectation of SEO/AI visibility gains from LLMs.txt alone.
- Mirror existing canonical pages; avoid pre‑release/beta/private endpoints.
- Review quarterly (or when IA changes) to prevent drift.
- Document policy internally (what qualifies, who updates, when).
What actually moves the needle for GEO
- Answer‑centric content: Clear sections that directly answer prompts, with evidence and quotable statements.
- Comparison‑first pages: Transparent criteria and extractable tables for “X vs Y” use cases.
- Structured signals: Clean titles, headings, summaries, tables, and appropriate schema.
- Technical foundation: Fast, crawlable, canonical pages with strong internal linking.
Frequently Asked Questions (FAQ)
Q: Should we implement LLMs.txt on our website?
A: Maybe, if it's low effort and you'll maintain it. LLMs.txt can help autonomous agents and RAG (Retrieval-Augmented Generation) systems find definitive resources faster. However, don't expect AI search visibility improvements from major platforms (ChatGPT, Claude, Perplexity, Google AI)—they don't use it for ranking or citations. Implement only if you have agent/enterprise AI use cases or want to standardize resource discovery across properties.
Q: Is LLMs.txt like robots.txt for AI crawlers?
A: No. Robots.txt provides enforceable crawl directives that control bot access. LLMs.txt is optional guidance that curates important pages—it's informational, not enforceable. AI platforms can ignore it completely. Think of it as a curated sitemap for agents, not an access control mechanism. Use robots.txt (and authentication) for actual access control.
Q: Does LLMs.txt improve visibility in Google AI Overviews or ChatGPT?
A: No current evidence supports this. Google's public guidance for AI Overviews says to follow normal SEO best practices—LLMs.txt isn't mentioned. Large-scale crawler log analysis shows occasional fetches but no correlation with citations or rankings. Focus optimization efforts on content quality, structure, E-E-A-T signals, and standard technical SEO for actual visibility gains.
Q: How do we measure the impact of LLMs.txt?
A: For agent workflows: track usage in internal enterprise AI tools and RAG systems (did agents successfully find resources?). For AI search: monitor brand citations and traffic patterns independently—don't attribute changes to LLMs.txt without A/B testing. Most businesses cannot measure LLMs.txt impact separately from broader GEO efforts. Treat it as optional housekeeping, not a measurable optimization lever.
Q: What should we include in LLMs.txt?
A: Only include pages you'd confidently recommend to an agent or researcher: comprehensive documentation, canonical product pages, pricing information, detailed FAQs, core blog content, comparison guides. Exclude beta features, internal tools, time-sensitive promotions, and sensitive information. Mirror your public sitemap—don't surface anything that isn't already publicly discoverable. Keep the list under 20-30 URLs for focus.
Q: How often should we update LLMs.txt?
A: Review quarterly or when information architecture changes significantly (new product launches, site restructures, canonical URL changes). Document who owns updates and what criteria qualify pages for inclusion. Avoid frequent changes—stability helps agents. If you can't commit to quarterly reviews, don't implement LLMs.txt—stale guidance is worse than no guidance.
Q: Can LLMs.txt hurt our SEO or GEO performance?
A: Not directly, but misconfiguration risks exist. Don't surface sensitive pages (admin areas, user data, internal tools), ephemeral content (limited-time offers, outdated product versions), or low-quality pages that contradict your SEO strategy. Ensure LLMs.txt aligns with robots.txt—don't list disallowed URLs. Stale LLMs.txt pointing to 404s or outdated content creates poor agent experience.
Q: What's the difference between LLMs.txt and XML sitemaps?
A: XML sitemaps list all indexable pages for search engine crawlers, typically thousands of URLs with technical metadata (lastmod, priority, changefreq). LLMs.txt curates a short list (10-30) of high-value "definitive resources" for agents, with human-readable notes. XML sitemaps are comprehensive; LLMs.txt is selective. Both can coexist—LLMs.txt doesn't replace sitemaps.
Q: Are major AI platforms planning to support LLMs.txt in the future?
A: Unknown. No major platform has announced plans to formally support or prioritize LLMs.txt for search ranking or citations. The specification remains community-driven and voluntary. Don't optimize based on speculation—focus on proven GEO factors (content quality, structure, authority, freshness). If platforms announce support, you can add LLMs.txt later without having missed significant opportunity.
Q: What are the best practices for LLMs.txt formatting?
A: Keep it simple and human-readable. Use YAML or plain text format with version number, URL list (absolute URLs only), and brief notes describing update cadence or canonical status. Avoid proprietary information, sensitive data, or implementation details. Place at web root (/llms.txt). Example: version: 1, urls: [list of 10-20 canonical pages], notes: "canonical resources; updated monthly". Test that all listed URLs return 200 OK responses.
Key Takeaways
- LLMs.txt is currently ignored by major AI search platforms (ChatGPT, Claude, Perplexity, Google AI Overviews) for ranking, citations, and content discovery—don't expect visibility gains from implementation
- Potential value exists for autonomous agent workflows, RAG (Retrieval-Augmented Generation) seed lists, and enterprise AI assistants that need curated starting points for definitive resources
- If implementing, keep minimal (10-30 URLs), accurate, aligned with public sitemap, and maintained quarterly—treat as optional metadata, not a GEO optimization lever
- Real GEO results come from answer-centric content, structured comparisons, clear hierarchies, E-E-A-T signals, technical excellence (speed, accessibility, structured data), and content freshness
- Risks include surfacing sensitive pages, ephemeral content, stale URLs pointing to 404s, or creating maintenance burden—only implement if you can commit to quarterly reviews
- Measure agent/RAG usage internally if implementing; don't attribute AI search visibility changes to LLMs.txt without controlled testing
- Focus optimization resources on proven factors (comprehensive content, expert authorship, frequent updates, structured formatting) rather than speculative or unproven tactics like LLMs.txt
Last updated: 2025‑11‑05
About the Author
Vladan Ilic
Founder and CEO
