Many teams invest heavily in RAG (Retrieval-Augmented Generation) systems to make their documentation searchable and answerable via AI. However, as this article points out, RAG excels at retrieval but fails at knowledge retention. It can find relevant documents but cannot capture the nuanced judgments and context that teams develop over time. The author advocates for an LLM Wiki approach, where teams actively curate and structure knowledge using AI assistance, creating a living document that evolves with the team's understanding. This hybrid model combines the best of retrieval and curation, ensuring that valuable insights are not lost. For engineering leaders and technical founders, this is a critical distinction: building a knowledge system that learns and retains context, not just one that fetches data.
An analysis of why RAG-only knowledge systems fall short and how an LLM Wiki approach can help teams capture and reuse contextual knowledge.