Evaluation Card

An evaluation card is a structured documentation artifact for an AI model evaluation, describing the benchmark used, the evaluation methodology, the prompts and conditions, the results, and the limitations. Evaluation cards extend the model card and system card tradition to the specific question of how a model was tested, recognizing that the same model can score very differently under different evaluation conditions. The need for evaluation cards became clear as benchmark contamination, prompt-format sensitivity, and judge-model bias issues emerged across the LLM evaluation landscape. Frameworks like HELM, EleutherAI's lm-evaluation-harness, OpenAI Evals, and Inspect AI (UK AI Safety Institute) produce evaluation outputs that function as evaluation cards. The 2024 OpenAI o1 system card and Anthropic Claude system cards include extensive evaluation card content alongside model documentation. AI governance teams require evaluation cards as evidence in AI compliance reviews because raw benchmark numbers without methodological context can mislead deployment decisions. The trend toward more detailed evaluation documentation parallels similar trends in clinical trial reporting and scientific reproducibility.

Evaluation-card-documented governance in Centralpoint: Centralpoint maintains evaluation documentation across whichever LLMs your stack uses, supporting AI compliance documentation and audit-readiness. Tokens are metered per skill, prompts stay local, and documented chatbots deploy through one line of JavaScript with audit-ready governance.


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