Auditor proposes evidence-backed case memory for AI teams losing RAG debugging knowledge
Additional-Ladder-24 · reddit · 2026-10-25
An engineer who audits RAG and retrieval systems proposes a product idea: a private, evidence-backed case memory for AI agencies and internal teams. Audit findings—retrieval failures, wrong citations, config changes, confirmed fixes—usually end up archived in reports, forcing others to debug from scratch. The envisioned system would let teams query past cases (similar symptoms, confirmed root causes, which fixes worked with evidence) and turn failures into reusable regression tests, with an LLM for case comparison and a separate evaluation engine for measurable checks. The author already has working components for persistent memory, hybrid retrieval, provenance tracking and RAG evaluation, but the full workflow is unbuilt and unvalidated, and is soliciting criticism before committing.
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