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Mostly about building products, healthcare tech, and lessons learned along the way.
2 articles on llm evaluation.

Most teams skip evals because the process feels overwhelming. The three steps that make eval-driven development achievable: label a small dataset, calibrate an LLM evaluator to human judgment, then iterate configs against the harness.

LLM-as-judge evaluators feel like quality assurance but behave like rubber stamps. They fail hardest on the outputs that matter most: edge cases, safety-critical errors, domain-specific nuance. What to do instead.