Lesson 260 of 1550
Public Benchmarks vs Private Evals: Why You Need Both
Public AI benchmarks (MMLU, HumanEval, etc.) tell you general capability. Private evals on your data tell you actual production fit. The smart teams maintain both.
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What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2benchmarks
- 3private evals
- 4model selection
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Section 1
The premise
Public benchmarks are necessary but insufficient; private evals on representative production data are what actually predict deployment success.
What AI does well here
- Maintain a private eval suite covering your specific use cases, edge cases, and adversarial scenarios
- Run new model versions through your private evals before deployment (don't trust vendor benchmarks alone)
- Track eval performance over time to detect drift
- Use eval failures to improve prompts, retrieval, or model choice
What AI cannot do
- Substitute private evals for production monitoring (different signal)
- Generalize eval results to scenarios not represented in the eval set
- Make evals exhaustive — they're a representative sample, not full coverage
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