Beyond Log Search: What We Learned Building a RAG-Based Incident Diagnosis System

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If you’ve ever been paged at 2 a.m. for a service degradation you don’t immediately recognize, you already know the real bottleneck in incident response. It’s rarely the fix. It’s finding out whether anyone has seen this before. Industry surveys indicate that 35–55% of incident-resolution time is spent on knowledge retrieval rather than remediation, involving the review of runbooks, old tickets, postmortems and internal wiki pages that often provide only partial answers. That’s not a tooling gap so much as a search problem wearing an incident-management costume. We spent the last several months building a system to close it, and the results were strong enough that we wrote them up as a peer-reviewed paper, accepted at IEEE GAISS 2026. This is the practitioner version of that work: What we built, what surprised us and what we’d tell another team considering the same thing. Why the Usual AIOps Toolbox Doesn’t Cover This Most AIOps tooling is good at a narrower job than people assume. Anomaly detection, log clustering, alert correlation — all of it operates on numerical or structured telemetry. Metrics, traces, log volumes: That’s useful, but it’s not where the knowledge actually lives. The thing that tells you “We saw this exact failure mode 18 months ago and the fix was a connection pool setting, not a redeploy” is sitting in a postmortem doc or a closed ticket, in plain English, not in a Prometheus series. Large language models (LLMs) are obviously good at plain English. The problem is that a standalone LLM asked to diagnose a production incident will happily generate a …

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