Writing documentation for AI: best practices

Summary

Retrieval-Augmented Generation (RAG) systems like Kapa rely on your documentation to provide accurate, helpful information. When documentation serves both humans and machines well, it creates a self-reinforcing loop of content quality: clear documentation improves AI answers, and those answers help surface gaps that further improve the docs. This guide provides best practices for creating documentation that works effectively for both human readers and AI/LLM consumption in RAG systems. Many best practices benefit both simultaneously, often in complementary ways. Why documentation quality matters​ Documentation quality has always been important for helping users understand and use your product effectively. And it becomes even more important when AI systems use that same content to answer user questions. Poor documentation doesn't just frustrate human readers, it directly degrades the quality of AI responses, creating a compounding problem where bad content leads to bad answers. Understanding how AI systems process and use your documentation reveals why content quality is non-negotiable for good AI performance. How AI systems process your documentation​ Kapa works by finding relevant pieces of your content and using them to construct answers. The process involves three main components: Retriever: Searches through your knowledge sources to find content that matches the user's question Vector database: Stores your content in a searchable format that enables fast and accurate retrieval Generator: A Large Language Model (LLM) that uses the retrieved content to create helpful responses Information flows through a specific process once you connect knowledge sources to Kapa: Ingestion: Content is divided into chunks (smaller, focused sections) and stored in the vector database Query processing: When users ask questions, the system converts their question into a searchable format Retrieval: The system finds the most relevant chunks from your documentation Answer generation: T...