Table of Contents
Software engineering has CI. AI deployments rarely do. The result: silent quality regressions when models, prompts, or RAG configs change.
TL;DR
Build a 200-prompt eval harness scored by an LLM judge. Run on every model / config change. Block deploys on >3% regression. The single highest-leverage practice for AI quality.
Why eval-driven
- Catches regressions before users do
- Justifies model upgrades
- Documents quality over time
- Required for any compliance review
Setup
- Build 200-prompt gold set (representative of production traffic)
- Score each response with LLM judge (Claude 3.5 Sonnet)
- Establish baseline scores
- Run on every config change
- Alert on >3% regression on any individual category
Verdict
Eval-driven development is the single most underused practice in AI deployment. Build it before you ship.
Bottom line
Eval first, deploy second. See LLM eval pipeline and RAG eval.
