Production-Grade AI Eval Systems. What I Learned Putting LLMs on Call
Production-grade AI reliability requires more than uptime and latency. A layered eval system helps teams detect hallucinations, RAG failures and quality regressions before customers do.
On a Thursday evening, the AI support agent had been live for three weeks and displayed green lights for latency and error rate. However, a customer's Slack message revealed a critical issue: the AI bot had fabricated a refund policy for a product that the company had never sold. The system had returned a confident response in 1.2 seconds, while every SRE metric indicated that the system was functioning properly.
Recognizing the severity of the situation, the author began developing a production-grade evaluation system to address the problem. This system aimed to prevent the silent changes and degradation of AI features that could negatively impact customer experience. The author emphasizes the importance of evaluating AI features in three explicit places: Phase 1, Phase 2, and Phase 3.
Phase 1 involves a quality baseline and a rubric, while Phase 2 consists of regression suites. Phase 3 is the most crucial, as it involves evaluating failures that were not originally predicted. The author also introduces the concept of a four-layer evaluator stack, consisting of deterministic code, LLM judges, domain checks, and human-in-the-loop evaluations.
This layered approach allows for the detection of failures that may not be caught by a single quality metric. The author stresses the importance of separating guardrails and evaluators into different code paths to prevent confusion and ensure that AI products are truly production-ready.
Written by urgent.news from DevOps.com's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
