How to build an evaluation harness before you build the AI system. Held-out test sets, scoring rubrics, tool selection (Inspect AI, Braintrust, Promptfoo, LangSmith), production observability, three worked examples at different scales, anti-patterns, and what month-four operational reality looks like.
The demo is always good. The version still running in month four is always less impressive than the demo. The thing that decides which version you end up with is almost never the thing that gets shown in the demo — it’s the evaluation suite. Build it before the system, not after.
This guide is the practical handbook we’d hand a new technical lead before they ship their first AI system in production. It covers held-out test set design (where cases come from, how many to write, what makes them representative), scoring rubrics that work (binary vs. graded vs. multi-dimensional, calibrating LLM-as-judge), tool selection across the 2026 eval landscape (Inspect AI, Braintrust, Promptfoo, LangSmith, Galileo), production observability (sample-and-score patterns, alerting, the tools that handle it), three worked eval setups at different scales, common anti-patterns, and the operational reality of running an eval suite for two years. Includes a starter template you can adapt for your own system.
In the guide:
Engineers, tech leads, and AI builders shipping production systems. Useful as a starter handbook before a system goes live, or as a structured way to evaluate whether an existing system has the eval and observability discipline to survive month four.
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