LLM Wiki - Compiled Knowledge That RAG Cannot Replace
The article discusses the limitations of Retrieval-Augmented Generation (RAG) systems in knowledge management. It introduces the concept of an LLM Wiki, which compiles knowledge at ingest time rather than retrieving fragments at query time. This approach aims to create a more structured and stable understanding of knowledge, addressing the weaknesses of traditional RAG systems.
- ▪RAG systems retrieve knowledge at query time but often treat it as a collection of fragments rather than a structured model.
- ▪An LLM Wiki uses a language model to synthesize and structure knowledge at ingest time, creating more coherent knowledge artifacts.
- ▪The LLM Wiki approach can improve cost, latency, quality, maintenance, governance, and failure modes compared to RAG.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3544400) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Rost Posted on May 18 • Originally published at glukhov.org LLM Wiki - Compiled Knowledge That RAG Cannot Replace #wiki #knowledgemanagement #rag #aisystems The premise is simple: compiled knowledge is more reusable than retrieved fragments. RAG became the default answer to a straightforward question - how do I give an LLM access to external knowledge? And the usual architecture is by now familiar.
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