SOPs for RAG: How to Prepare Your Library for AI Retrieval

RAG (retrieval-augmented generation) lets an LLM answer a question by first fetching the most relevant SOP from your library, then generating a response grounded in that SOP. The quality depends on three things: how you chunk each SOP, what metadata you attach, and which embedding model you use — not the LLM itself. In 2026 the emerging default is one SOP per source document, chunked by section heading (150–400 tokens per chunk), with metadata tags for trigger, owner, and last-updated. [Document360](/reviews/document360) and [Haiku](/reviews/haiku) both expose RAG-ready APIs; other tools require a custom export pipeline. See our [AI agents and SOPs](/blog/ai-agents-and-sops) post for how agents actually consume the retrieved content.