Evaluation & eval-driven development
The keystone skill. Own this and you own quality.
If you can prove an AI system is good and catch when it regresses, you own quality — and quality is your authority across teams. Most teams can't do this. Make evals the heartbeat of every AI feature, including your own AI-SDLC pipeline.
Key ideas
- 1
Eval-driven development: define how you'll measure success BEFORE building, then iterate against it — like test-driven development for AI.
- 2
Start with error analysis: read real outputs, label failure modes, and let those define your metrics. Don't start from generic benchmarks.
- 3
Build a golden dataset of representative inputs with expected behavior; run it in CI so a prompt/model change that regresses fails the build.
- 4
LLM-as-judge scales evaluation but has pitfalls (bias, inconsistency) — calibrate judges against human labels and prefer pairwise comparisons for subtle quality.
- 5
Combine offline evals (regression suites) with online signals (A/B tests, guardrail metrics, human feedback) from production.
How to start (this week)
- Collect ~50–100 real inputs covering the important cases and edge cases.
- Read outputs and do error analysis: cluster failures into named categories.
- Turn the top failure categories into checks (assertions, rubrics, or judge prompts).
- Wire it into CI; track scores over time; gate risky changes.
Metrics that fit the task
- RAG: retrieval recall/precision, faithfulness/groundedness, citation accuracy.
- Classification/extraction: precision/recall/F1 against labels.
- Generation/agents: task success rate, rubric scores, pairwise win-rate vs a baseline.
- Always track cost and latency alongside quality.
Watch
Do the work
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