Are We Letting Software Decide What AI Should Know About Our Products?

Files like llms.txt are creating a new editorial responsibility for tech writers

I didn’t pay much attention to llms.txt it first started appearing on documentation sites. At first glance, it looked like another generated file that did something I didn’t need to understand at the time.

Then I realized something was different. Those files mostly help systems discover or process information that’s already there. llms.txt exists to help AI systems understand our documentation.

For decades, tech writers have made editorial decisions about what customers should know. We decide which concepts deserve an explanation, which procedures belong (or don’t) in our product docs, and which content should be retired because it no longer reflects reality.

Today, many publishing platforms automatically generate a document whose audience isn’t your customer at all. Instead, it’s AI.

This Isn’t Just Another Generated File

An llms.txt file is often described as a guide for AI systems. That’s accurate, but incomplete. It’s much more than that. You can think of it as a summary of what your organization considers important enough for AI to read first.

Depending on how your documentation platform generates it, the file may point to product overviews, API references, tutorials, SDKs, migration guides, code examples, and other documentation. An AI system can use that information to build an initial understanding of your product long before it retrieves content from any individual page.

That’s a different editorial function than a sitemap.

👉🏾 A sitemap says, “These pages exist.”

👉🏾 An llms.txt file effectively says, “Start here.”

Automatic Doesn’t Mean Curated

Recently, I examined the llms.txt file for a public API documentation site. Nothing in it was confidential. Every page it referenced was already public.

What surprised me wasn’t the content. It was how much easier the file made it to understand the product.

Instead of exploring the documentation one section at a time, I could immediately see the major APIs, supporting guides, integration points, and product organization. The documentation had been distilled into a machine-readable map.

That’s exactly what the file was designed to do.

The question is whether that map reflects an editorial decision or simply the default output of a publishing platform.

Those aren’t the same things.

An automatically generated file may include deprecated documentation, legacy APIs, archived migration guides, experimental features, or transitional content simply because those pages are still public. The software doesn’t know which content best represents your product today. It only knows what we tell it.

This Is An Editorial Question

Tech writers are the people who decide how a product should be explained. We choose examples, organize information, and retire obsolete content. We often work with product managers, software developers, support teams, and legal reviewers because we understand that publishing is an editorial process, not a database export.

I think llms.txt belongs in that process. If AI systems are going to use it as a starting point for understanding our documentation, then someone should review what it communicates before its published.

Not because AI shouldn’t read your documentation. Because we should be intentional about what we’re helping it understand.

A New Publishing Checklist

Most documentation teams already review titles, links, examples, screenshots, and metadata before publishing. It may be time to add one more item to that checklist.

👉🏾 Review the documentation we’re publishing for machines with the same care we give the documentation we’re publishing for people.

A publishing platform can generate an llms.txt file automatically. Only we can decide whether it tells the right story about our product. 🤠

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Note: The llms.txt specification is evolving through community input. Its GitHub repository provides version control, issue tracking, and public discussion, while a community discord channel gives implementers a place to share experiences, compare approaches, and discuss emerging best practices.