Building Great Evals: Avoid Single Scores and Embrace Hill Climbing
realmadhuguru · x · 2026-08-23
This series covers building AI evaluations (evals).
Part 5: The Tyranny of the Average
Avoid simplifying complex eval suites into a single score. AI quality is multi-dimensional; weighted averages obscure performance regressions in key frontier use cases (e.g., complex analysis). Decisions should rely on granular metrics, not a single number.
Part 6: Hill Climbing on Evals
Hill climbing means selecting key dimensions to optimize. The actual work involves prompt engineering, context engineering, memory, post-training, or deterministic code. Use your failure mode taxonomy (from Part 3) to identify struggles. For example, if tool calling fails often, optimize context engineering to provide the right 3-5 tools instead of stuffing 20 into context.
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