Five hard earned lessons about Evals
summary
Eval competence is measured: an eval that lets a team launch update within 24 hours after a new model proves its value, and a clear pipeline that turns a user complaint into a test case prevents lost signal. Good evals are engineered: custom scorers as product specs (auto evals is flexible but many teams write their own), continuous reconciliation of datasets, and context engineering—tool definitions and outputs dominate tokens so switching tool output from JSON to YAML can cut tokens and improve LLM analysis. New models can flip viability overnight—Cloud 4 Sonnet moved a feature from 10% to production in two weeks—and optimizing prompts plus data plus scorers together (Brain Trust proxy and the new loop autooptimizer) matters more than tweaking prompts alone.
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chapters (7)
- 0:00Introduction to 5 Lessons in AI Product Development
- 0:19Lesson 1: Effective Evals Speak for Themselves
- 2:09Lesson 2: Great Evals Need to Be Intentionally Engineered
- 4:03Lesson 3: Context Engineering is the New Prompt Engineering
- 6:37Lesson 4: Be Prepared for a New Model to Change Everything
- 9:09Lesson 5: Optimize the Entire Evaluation System, Not Just the Prompts
- 12:21Recap of the Five Lessons
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