Writing Docs in a World Where LLMs Are the Readers

Writing documentation is no longer just for humans.

Developers still read it, but AI reads it too. Large language models scan, summarize, and even generate code from your docs.

This shift does not make documentation less important, it changes how we write and structure it. In this article, you’ll learn how llms.txt helps your docs work for both humans and machines.

1. How Google Cloud Is Adapting Documentation for AI

The Google Cloud Developer Experience team focuses on one goal: helping developers move from learning to launching as fast as possible.

As Google Cloud services grew, keeping documentation accurate and up to date became harder. Developers expect quick, correct answers. If docs fall behind, adoption suffers.

Google Cloud did not replace technical writers.

They augmented them with AI.

Generative AI is now part of their documentation workflow. It helps with formatting, markup translation, and validation. Some docs are even tested automatically by running the documented steps in real environments.

Documentation is treated like code: generated, tested, and continuously improved.

You may not work at Google Cloud scale, but the same pressures already exist in many teams today.

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2. Documentation Is No Longer Read Only by Humans

Developers still read documentation. But very often, AI reads it first.

Today, developers:

• Ask AI to generate and debug code
• Let AI research APIs and tools
• Paste full documentation pages into prompts

Human readers are still important. But LLMs are now a primary consumer of documentation.

Documentation is no longer just read by humans. It is consumed by LLMs.

That reality changes how docs should be structured and published.

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3. Tech Writers Do Not Compete With AI. They Enable It.

It is easy to worry that AI will replace tech writers.

In practice, the opposite is happening.

AI can generate text quickly. It cannot decide what matters, what is correct, or how concepts should be structured.

Tech writers provide that structure.

Tech writers do not need to compete with AI. They need to organize knowledge so AI can use it correctly.

This shift moves the role from writing pages to designing knowledge systems.

One common way to do this is by providing AI tools with a structured, machine-readable version of your docs. This is where llms.txt comes in.

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4. What llms.txt Is and What It Is Not

llms.txt is a machine-readable version of your documentation. It is usually written in Markdown and designed for AI tools and LLMs.

Think of it as a translation layer:

• Your main documentation stays human-friendly
• llms.txt gives AI a clean and structured view of the same content

A good llms.txt file often includes:

• Core concepts and terminology
• API overviews and constraints
• Authentication and environment assumptions
• Canonical examples
• Known limitations and edge cases

What it is not is just as important.

This does not replace documentation.

It protects it.

By giving AI its own context file, you avoid turning human docs into prompt-shaped content. Human readers get clarity. AI tools get structure.

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5. Make llms.txt Auto-Generated

One key lesson from Google Cloud is automation.

Their documentation is generated, validated, and tested continuously. llms.txt should follow the same idea.

Best practice is to auto-generate it whenever documentation changes.

Practical guidance:

1. Generate llms.txt as part of your docs build process
2. Regenerate it on every docs edit
3. Preserve headings, code blocks, links, and examples in Markdown
4. Add simple checks to ensure the file is complete

This matters because:

• AI relies on fresh context
• Manual updates drift over time
• Automation keeps humans and AI aligned

One source of truth.

Two audiences.

No duplication.

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6. Lessons from Google Cloud’s AI Code Systems

Google Cloud also applied AI to code samples.

They faced thousands of APIs, many languages, and constant change. Manual maintenance did not scale.

Their solution used AI systems that:

• Generate samples from official API definitions
• Review and refine results automatically
• Test code before publishing

The lesson is simple.

AI works best when knowledge is structured, grounded, and validated.

That same principle applies to documentation. llms.txt provides that structure for AI tools.

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7. How to Use llms.txt in Practice

For AI tools with limited capabilities that cannot fetch docs on their own, llms.txt is especially useful.

A simple workflow:

1. Open docs.example.com/llms.txt
2. Download/Copy the Markdown file
3. Upload it into your AI coding tool
4. Ask the tool to analyze, debug, or generate code using this context

This keeps AI output aligned with real documentation and real constraints.

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8. Make It Easy to Find

For llms.txt to be useful, it must be visible.

Recommended approach:

• Publish it at docs.example.com/llms.txt
• Keep it in Markdown format
• Add a visible button in your docs like “AI Context (llms.txt)”
• Open it in a new tab

This is not a power-user trick.

It is basic documentation infrastructure.

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9. Closing Thoughts

AI is not removing the need for tech writers.

It is raising expectations.

The work shifts from writing more pages to:

• Structuring knowledge clearly
• Designing systems that scale
• Making documentation reliable for humans and machines

llms.txt is a small file, but it represents a real shift.

If you own documentation today, the question is not whether AI will read it.

It already does.

The real question is whether it is reading the right version.