Writing Docs for AI: Making Your Product Seamless for Cursor, Windsurf, and Claude Code Users
Developers increasingly rely on AI-powered tools like Cursor, Windsurf, and Claude Code to streamline coding, debugging, and automation. But if you want your product to integrate smoothly into these AI-driven workflows, you need to structure your documentation in a way that's optimized for Large Language Models (LLMs).
In this post, I'll share how we structure our documentation for ActorCore to make it easy for AI-powered tools to understand and use.
Why Write Docs for AI?
AI-powered coding assistants thrive when they have clear, structured, and accessible documentation. The more explicit and machine-readable your docs are, the better these tools can:
- Autocomplete API calls and function signatures
- Suggest accurate implementations based on your SDKs
- Troubleshoot common integration issues
- Reduce friction for developers using your product
By structuring your documentation to be AI-friendly, you help AI assistants guide developers more effectively—lowering support requests and increasing adoption.
How to Structure Docs for AI
LLMs like the ones powering Cursor, Windsurf, and Claude Code need information in a way that is both human-readable and structured for AI parsing. Here's how to do it:
1. Provide a prompt.txt for AI Context
The prompt.txt file acts as a cheat sheet for LLMs, helping them understand your product's conventions. It should include:
- Naming conventions (how APIs, methods, or objects are named)
- Project structure (modules, dependencies, key files)
- Common commands and use cases
- Error-handling approaches
- How to use your SDK in real-world scenarios
This helps AI tools stay on track when assisting developers, ensuring they generate useful, on-brand code.
2. Use llms.txt for AI Optimization
llms.txt is designed for AI reasoning engines to index and understand your project. This file provides:
- Metadata about your product
- Context on how it should be used
- Best practices for integration
By including llms.txt in your docs, you allow AI-powered assistants to ingest and recall relevant details, making their suggestions more aligned with your ecosystem.
3. Markdown-Based Docs for LLMs
A common format that AI tools parse well is Markdown. Hosting your docs in an LLM-friendly format like Docs as Markdown ensures that:
- AI tools can easily read and interpret the content
- Devs using Cursor/Windsurf get faster, more relevant code suggestions
- Formatting stays consistent and structured
4. Make Your Product Work Seamlessly with Cursor & Windsurf
Developers using AI-assisted coding environments like Cursor and Windsurf benefit when your documentation is optimized for AI readability.
How to improve integration with these tools:
- Offer code snippets that AI can easily copy and modify
- Write function descriptions in a structured way
- Include common errors and fixes so AI can debug automatically
- List API capabilities explicitly so AI tools don't have to infer
By formatting your docs to work well with AI-powered coding assistants, you reduce friction for developers integrating your product into their workflow.
TL;DR – Build AI-Readable Docs for Better Dev Adoption
If developers are using AI-powered tools like Cursor, Windsurf, and Claude Code to build with your product, your docs need to be AI-friendly.
- Use prompt.txt to train AI assistants on your product
- Add llms.txt for AI metadata indexing
- Write docs in Markdown for better AI parsing
- Ensure Cursor and Windsurf can seamlessly pull relevant info from your docs
By structuring your documentation for AI, you make it easier for developers to use your product—without requiring them to read every page manually. The AI does the work for them, and that's the future of documentation.