OpenAI’s Evaluation Playbook Puts Harness Design at the Center of Model Testing

Short summary

OpenAI's evaluation playbook argues that benchmark results reflect the entire testing harness — prompts, tools, compute budgets, state management, and scoring — not just the model itself. The framework separates capability elicitation, safeguard performance, and model comparisons, urging evaluators to document their setup so claims remain interpretable. For developers, the key takeaway is that benchmark scores do not automatically transfer across different deployment environments.

• •Harness design (prompts, tools, budgets, scoring) materially affects model evaluation results
• •OpenAI distinguishes capability elicitation, safeguard performance, and model comparison claims
• •Benchmark scores should not be treated as transferable properties across deployments

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