Verifiable Context Governance for Autonomous AI Agents | PromptOwl
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Empirical results from peer-reviewed research co-authored with Benn R. Konsynski. Learn how hash-chained context networks achieve 97% quality and 3x token reduction.

Original Reddit post

Karpathy’s LLM Wiki pattern argues for structured markdown over RAG. This study measures what governance adds to that architecture. Under stale-document conditions — where old versions remain in the retrieval pool after an update — governed context selection hit 97% answer-quality pass rate. BM25 sparse retrieval: 90–93%. At roughly one-third the input-token cost. Better answers, lower cost — sounds like a winning pattern to me. Full disclosure: I work at PromptOwl, the maker of ContextNest and Community ContextNest (the team version), and the research was a joint effort using ContextNest with Emory University and IBM Research. submitted by /u/EcstaticRead9321

Originally posted by u/EcstaticRead9321 on r/ArtificialInteligence