Lenny's Reads

Building eval systems that improve your AI product

28 snips
Sep 9, 2025
Discover the crucial strategies for improving AI products through robust evaluation systems. Learn why traditional dashboards often fail and how error analysis can reveal critical failure modes. Hear about the importance of a principal domain expert in maintaining quality standards. Explore the effective use of human labeling for ground-truth datasets and why binary evaluations outperform Likert scales. Delve into techniques for debugging AI workflows and fostering continuous improvement in complex systems. This discussion is packed with insights for engineers and product managers!
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INSIGHT

Measure Real User Failures

  • Most eval dashboards fail because they measure fashionable metrics disconnected from real user problems.
  • Error analysis grounded in real data reveals where your product actually breaks and what to prioritize.
ADVICE

Use A Principal Domain Expert

  • Appoint a single principal domain expert to be the arbiter of quality for early-stage evals.
  • Use a representative set of ~100 user interactions to ground their judgments and develop intuition.
ADVICE

Do Open Coding With Binary Labels

  • Perform open coding: write freeform critiques and a binary pass/fail for each interaction.
  • Make critiques detailed enough for a new hire or few-shot LLM prompt to understand.
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