LLM Wiki v2
LLM Wiki v2 introduces a refined approach to building personal knowledge bases with LLMs. It emphasizes the importance of memory lifecycle, confidence scoring, and knowledge retention to enhance the utility of wikis. The document also outlines the need for a structured knowledge graph to improve information retrieval and organization.
- ▪The original LLM Wiki concept is expanded with lessons learned from practical application in building a persistent memory engine.
- ▪A key insight is that knowledge should have a lifecycle, with newer information automatically superseding older claims.
- ▪The proposed system includes confidence scoring for facts, allowing the LLM to assess the reliability of information based on source support and recency.
Opening excerpt (first ~120 words) tap to expand
LLM Wiki v2 A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory 10K Stars ⭐️, a persistent memory engine for AI coding agents. This builds on Andrej Karpathy's original LLM Wiki idea file. Everything in the original still applies. This document adds what we learned running the pattern in production: what breaks at scale, what's missing, and what separates a wiki that stays useful from one that rots. Currently, Working on AKBP: Agent Knowledge Base Protocol based on my findings, a protocol for creating, updating, retrieving, and sharing durable knowledge across AI agents. What the original gets right The core insight is correct: stop re-deriving, start compiling. RAG retrieves and forgets. A wiki accumulates and compounds.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Gist.
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