Production-Grade AI Eval Systems

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It was around 11 p.m. on a Thursday. Our AI support agent had been live for three weeks. Latency: Green. Error rate: Green. Then a customer DM landed in Slack with a screenshot — the bot had cheerfully invented a refund policy for a product we had never sold. Made up the SKU. Made up the rules. Returned a confident answer in 1.2 seconds. Every SRE metric said the system was healthy. The system was lying to customers at scale, and we had no signal. That night I started building what I now call a production-grade eval system. This is the version I wish someone had written for me before that Thursday. The Honest Problem For 15 years, the SRE playbook worked because systems were deterministic — same input and output. LLMs break that contract. Your vendor can silently push a new model checkpoint on a Wednesday, and your agent develops a new personality. A re-indexed retrieval store can send ‘what’s your return policy?’ to a marketing blog post instead of the actual policy doc. None of those registers as a 4xx. The result: Dashboards all green; product quietly degrading. Everything below exists to close that gap. The Three Places You Must Evaluate Most teams vibes-check their AI feature: A PM tries six prompts, says “feels good” and ships. There’s no regression suite, no quality baseline and no rubric. The right fix is to evaluate in three explicit places:

Most teams have Phase 1, sort of. Almost nobody has Phase 2. Phase 3 is where the screenshots-in-Slack live. If I could pick only one, I’d start with Phase 3 — it catches failures you didn’t predict. You need all three to …

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