LLMs.txt - Speculative hype or strategic edge?

Marketers: have you created an llms.txt file for your brand website? If you haven’t, I’ll share what it is, how it differs from robots.txt, and the ways it might help AI find and represent your brand.

But first, I checked 20+ top B2B SaaS websites. (Searched website.com/llms.txt).

Zero had an llms.txt file installed. 👀

In the past year, I saw a brand site go from virtually 0 LLM referral traffic to over 25% coming from ChatGPT, Claude and Perplexity.

A lot of that came from content strategy (a post for another day), but I wanted to make sure that content was being cited and represented accurately. That’s where llms.txt felt like a natural next step.

What it is

Llms.txt is similar to robots.txt, but instead of controlling crawler access, it guides AI models to the pages that best represent your brand.

Think of it as a treasure map for AI — a simple text file that tells large language models which pages matter most and how to interpret them.

Here’s why that matters:

As AI assistants replace traditional search, they’re pulling answers directly from the web. If you don’t signal your most accurate content, AI might quote someone else’s version of your story.

How to set it up

✅ Put it in your root directory

EX: yourdomain.com/llms.txt — just like robots.txt

✅ Start with a brand summary

Explain who you are, what your site covers, and why your content is authoritative

EX: “M+M helps B2B SaaS brands build marketing engines with measurable growth impact”

✅ List your top pages

Include cornerstone content: About, product, pricing, case studies, FAQs, and data studies. Add short descriptors so AI understands relevance.

✅ Keep it simple and updated

Avoid listing every URL. Prioritize quality over quantity. Review quarterly.

✅ Make those pages LLM-friendly

Use clear headers, concise paragraphs, and “Key Takeaway” callouts. Models prefer structured, scannable content.

Why marketers should care

It’s brand control in an AI-driven world.

It’s visibility when assistants replace search.

And it’s early-mover advantage — almost no one is doing it.

There’s no real downside. llms.txt won’t spike traffic today, but it could influence how AI systems discover, cite, and summarize your brand tomorrow.

Think of it as a low-risk, strategic experiment in the early AI-answer-engine era, not a guaranteed channel. The cost is minimal. The potential upside is owning how your brand is represented as this new layer of discovery evolves.

What we know (and don’t)

In my research, I couldn’t find major consumer-facing sites using it yet. Most early adopters are developer or documentation-heavy companies:

• Zapier lists its API docs through llms.txt and llms-full.txt

• Stripe uses it for product and documentation pages

• Cloudflare has one of the most detailed implementations

• Mintlify has published examples and tools

There’s no public evidence yet that llms.txt improves traffic, leads, or brand accuracy. None of the major LLM providers (OpenAI, Anthropic, Google) have confirmed they read or prioritize it.

Ahrefs has one of the clearer overviews: ahrefs.com/blog/what-is-llms-txt

So for now, it’s smart experimentation, not proven growth.

How to track efficacy

If you decide to test it, track what you can:

• Changes in referral traffic from AI tools (ChatGPT, Perplexity, Claude)

• Brand mentions and accuracy in AI-generated answers

• Consistency of information across your key pages

For now, success looks like improved visibility and accuracy, not immediate conversion lift.

My questions to you:

- Are you aligning on this as a new standard?

- Is it an early-mover advantage worth the effort?

- And has anyone seen measurable benefit yet?