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Release gates for LLM systems

2 min readUpdated 2026-09-09

An AI feature should not be promoted because a handful of examples look impressive. It should be promoted when the evidence package explains what it does well, where it fails, and how the team can recover.

Public-safe engineering note · no client or confidential implementation details
01

Start with the task, not the model

A release gate begins with a task contract. What is the system expected to answer, classify, retrieve, or execute? Which cases are unacceptable even if the average score looks healthy? This framing keeps evaluation tied to user and operational outcomes rather than whichever benchmark is easiest to report.

02

Separate the gates

I prefer separate checks for retrieval, generation, security, latency, and operational behavior. Retrieval can fail while the model remains fluent. A response can be relevant while leaking an unauthorized field. A system can be accurate but too slow or expensive for its intended workflow. One composite score hides those differences and makes ownership unclear.

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01 / SYSTEM VIEWInspect the architecture Follow a request through retrieval, validation, and audit.02 / EVIDENCEReview the case studies See the engineering decisions and measured outcomes.03 / OPEN CHANNELStart a conversation Talk through a production GenAI problem.

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