Improving Knowledge Base Quality for RAG Systems: Best Practices for AI and Humans
This guide outlines best practices for creating documentation that effectively serves both human readers and AI/LLMs in Retrieval-Augmented Generation (RAG) systems. High-quality documentation improves AI responses and user experience, creating a positive feedback loop. The article details how AI systems process documentation (retrieval, vector database, generation), highlighting the importance of clear, concise, and contextually complete content. Recommendations include using semantic HTML, avoiding PDFs, creating crawler-friendly content, ensuring semantic clarity, providing text equivalents for visuals, and maintaining simple layouts. The guide also addresses common content design challenges like contextual dependencies, semantic discoverability gaps, implicit knowledge assumptions, and visual information dependencies. It advocates for a hierarchical information architecture, self-contained sections, and providing error context with solutions. Ultimately, the goal is documentation that is both human-readable and AI-friendly.
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