A team I worked with had a system changing faster than they could spread the knowledge around the company.

Every consultant says “build a RAG.” What they leave out is the data pipeline underneath it: engineering effort, time, money.

The team uses Elixir, which keeps documentation next to the code and exports it as Markdown. I exposed that documentation through /llms.txt, a plain text file at a known URL that tells AI tools where to find your docs.

Another microservice? Another line in the file.

Anthropic does this for Claude’s own docs. Ask Claude about its features and it uses the claude-code-guide skill, fetching Markdown from the public internet.

Within a couple of hours, product managers and analysts could see exactly how the product behaves in production, with no new tools.

Even in AI, fundamentals win.

“What should I put in my CLAUDE.md” is the question I hear most from engineers trying to speed up their work.

The common mistake is treating it as documentation for product managers. “We’re explaining what the code does anyway, so we’ll use it as docs.” Wrong.

Give Claude directions for finding the right information.

Bad: “In the class Calculator I export the methods sum and subtract.”

Good: “To do math, look at the Calculator class.”

Mid-sized companies treat documentation as nice to have. Agile made it feel useless, so teams stopped writing it.

Your agents need context to work across your stack. That makes documentation a moat.

Stop asking agents to “research this codebase thoroughly.” Document it once. Save the tokens.