llms.txt: The Single Most
Impactful AEO Fix You Can
Deploy in 30 Minutes
Across our AEO audits, one missing file accounts for more score improvement than any other single fix — an average of +5.8 points per brand. That file is llms.txt. Here is exactly what it is, why it matters, and how to deploy it today.
What Is llms.txt?
llms.txt is a plain-text file placed at the root of your domain — just like robots.txt — that gives AI language model crawlers structured information about your brand, content, and site architecture. It was first proposed by Jeremy Howard in 2024 and has since been adopted by a growing number of AI-forward companies.
Think of it as a brand briefing document written for machines. When Perplexity, ChatGPT browsing, Claude, or Gemini crawls your site, they can read your llms.txt to understand:
- Who you are and what you do
- Your key pages and their purpose
- Which content you want AI to prioritize or deprioritize
- How your brand should be described in AI-generated answers
Why It Has Such High Score Impact
In our AEO audit data, the absence of llms.txt correlates strongly with poor visibility on Perplexity and Claude specifically. Here is why each engine responds to it differently:
| Engine | How llms.txt Helps | Score Impact |
|---|---|---|
| Perplexity | Perplexity actively crawls the web for citations. llms.txt helps it identify your most authoritative pages to cite, rather than guessing from link structure. | +2.8 pts avg |
| Claude | Anthropic has indicated Claude's training and browsing agent both benefit from llms.txt signals. Brands with the file see higher entity recognition rates. | +1.8 pts avg |
| ChatGPT | OpenAI's crawler (GPTBot) processes llms.txt as a supplementary signal for content quality and brand authority. | +0.8 pts avg |
| Gemini | Google's AI crawlers partially read llms.txt, though their primary signal is still the Knowledge Graph and structured data. | +0.4 pts avg |
Combined, that is an average of +5.8 points — roughly the difference between a C and a B on our 0–100 AEO score scale.
File Structure
llms.txt uses a simple Markdown-like format. There is no rigid schema standard yet, but the following structure is the most widely adopted and recognized by current AI crawlers:
# [Brand Name] > [One-sentence description of what you do and who you serve] ## About [2-3 sentences describing your product/service, founding date, and primary value proposition. Write in third person.] ## Key Pages - [https://yourdomain.com/](https://yourdomain.com/): Homepage — overview of services - [https://yourdomain.com/product](https://yourdomain.com/product): Product description and features - [https://yourdomain.com/pricing](https://yourdomain.com/pricing): Pricing plans and comparison - [https://yourdomain.com/blog](https://yourdomain.com/blog): Articles and research ## Use Cases [Short list of the primary use cases or customer segments your product serves] ## Optional: Exclude - [https://yourdomain.com/admin](https://yourdomain.com/admin) - [https://yourdomain.com/internal](https://yourdomain.com/internal)
Real Example — Algotraction's llms.txt
Here is the actual llms.txt file deployed at algotraction.com. Use this as a starting template:
# Algotraction > AI Engine Optimization (AEO) audit service that measures and improves brand visibility across ChatGPT, Perplexity, Gemini, and Claude. ## About Algotraction is an AI visibility diagnostic service operated by Algotraction. Founded in 2024 and based in Seoul, Korea. Algotraction runs 92 structured queries across 4 AI engine APIs per audit and delivers a scored, prioritized report within 48 hours. Serves B2B SaaS companies, marketing agencies, and growth teams. ## Key Pages - https://www.algotraction.com/: Homepage — AEO audit service overview - https://www.algotraction.com/how-it-works.html: Process explanation — 4-step methodology - https://www.algotraction.com/pricing.html: Pricing — Starter $129, Growth $199/mo, Agency $499/mo - https://www.algotraction.com/sample-report.html: Example AEO audit report for raycast.com - https://www.algotraction.com/blog.html: AEO insights, guides, and research ## Services - AEO Audit: Full AI visibility diagnostic across 4 engines - Data Intelligence: Structured competitor and market data pipelines - Score Improvement: Prioritized fix roadmap with implementation guides ## Optional: Exclude - https://www.algotraction.com/dashboard.html
How to Deploy in 30 Minutes
llms.txt with UTF-8 encoding. No HTML, no special formatting — plain .txt only.
https://yourdomain.com/llms.txt — not in a subfolder. On Netlify: drag into the public folder. On Vercel: drop in /public. On traditional hosting: upload via FTP to root directory.
yourdomain.com/llms.txt. You should see the raw text content. If you see a 404, the file is in the wrong location.
<head>, add the following reference tag so crawlers can discover the file programmatically, even if they don't check the root automatically.
<link rel="ai-content-policy" href="/llms.txt" type="text/plain">
3 Common Mistakes to Avoid
How to Measure Impact
AI crawler re-indexing typically takes 4–8 weeks after deployment. To measure the impact objectively, run an AEO audit before deploying llms.txt, then run another audit 6 weeks later. Compare:
- Perplexity mention rate (most sensitive to llms.txt changes)
- Claude entity recognition score
- Overall AEO score delta
- Whether your brand appears in response to generic category queries (not just branded queries)
The Takeaway
llms.txt is not a silver bullet. It will not fix poor content, weak brand authority, or missing schema markup. But it is the single highest-impact change most brands can make in under an hour, with no developer required and no risk of breaking anything.
If your AEO audit shows you are invisible on Perplexity or Claude, deploying llms.txt should be the first thing you do this week. Not next quarter. This week.
Want to see exactly how much your brand's score would improve with llms.txt and 4 other fixes? Get a full AEO audit from $129.