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How we stopped our AI assistant from hallucinating bug fixes

VIBEESH SUBRAMANIAN 2026年06月25日 14:43 1 次阅读 来源:Dev.to

Cover: a real qa-probe run against our own stack, cropped to the summary - internal product detail withheld. We are building LightShield, a SIEM that is in active demo right now. We built most of it pair-programming with an AI coding assistant wired in over MCP - it ran our stack, read the errors, and patched its own code. For a small team that is a superpower. Until an endpoint failed. Here is the loop we kept hitting. A route returns a 500, or a 404, or an empty [] . The assistant looks at the status code and announces the cause with total confidence. Then it rewrites a handler that was never broken - because a status code is not a cause, and it had nothing else to go on. So it guessed, and it guessed wrong, and the diff made things worse. The thing is, that empty [] had at least six possible causes: the database was empty (nothing seeded) a feature flag was off a contract mismatch between the frontend and the backend an auth token that never got attached a 428 precondition a schema drift Same symptom, six different fixes. We could bisect to the real one. The AI could not - it had no ground truth, so it manufactured one. So we built qa-probe It analyzes the app, probes the live endpoints, and classifies each failure with a root cause and a fix hint. Three decoupled, cached phases: qa-probe analyze # parse source + OpenAPI -> route graph qa-probe probe # hit live endpoints (HTTP/SSE/WS), record evidence qa-probe report # classify root cause -> HTML / Markdown / JSON / AI-context # or just: qa-probe run It has adapters for FastAPI, Express, Next.js, tRPC, GraphQL, and a generic fallback, so it discovers your routes instead of you hand-listing them. The part that actually fixed our problem: every result is falsifiable Each result carries the evidence (the real request, a bounded response sample, the timing), a root cause from ~25 categories, and a calibrated confidence - high , medium , or none . When it cannot tell, it returns none instead of bluffing. No neural net

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