Stage 6 of 9
Evals as Product Metrics
Measure quality the way users experience it and catch regressions before they ship.
Level: Advanced About 3 weeks at 6 hours a week 6 articles
By the end of this stage you can
- Build a golden dataset from real user tasks
- Design rubrics and calibrate an LLM judge
- Run eval regression suites in CI tied to business KPIs
Articles
- 6.1 Why evals are product metrics Evals measure how often an AI feature is good enough for users. Learn why they belong with product metrics, not only in an ML notebook. Level: Advanced 8 min Completed
- 6.2 Building golden datasets from real user tasks Build a golden dataset from real user tasks, covering sampling, coverage of hard cases, writing reference answers, privacy, and keeping the set fresh. Level: Advanced 9 min Completed
- 6.3 Rubrics and human grading Write rubrics that make AI output gradeable, run human grading sessions, and measure agreement between graders before trusting the scores. Level: Advanced 7 min Completed
- 6.4 LLM-as-judge: design and calibration Use a model to grade AI output at scale, with judge prompts, rubrics, calibration against human grades, and the biases to guard against. Level: Advanced 9 min Completed
- 6.5 Regression eval suites in CI Run evals in CI so prompt and model changes can't silently make an AI feature worse, with thresholds, flakiness handling, cost control and reporting. Level: Advanced 11 min Completed
- 6.6 Linking eval scores to business KPIs Connect offline eval scores to the business metrics they should move, check that the link holds, and report AI quality in terms leadership understands. Level: Advanced 8 min Completed