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OPENAI EVALS
LLM evaluation framework from OpenAI
MIT
ABOUT
Evaluating LLM outputs requires consistent, reproducible test methodologies, but teams often rely on manual review or ad hoc scripts that don't scale across model versions or use cases. OpenAI Evals provides a structured framework for defining evaluation tasks, running model-graded assessments, and composing test suites that give developers confidence in model quality before shipping to production.
INSTALL
pip install evalsINTEGRATION GUIDE
1. Run standardized evaluation tasks to measure LLM accuracy, safety, and instruction-following
2. Use model-graded evaluations for assessing open-ended outputs like summaries and translations
3. Build custom evaluation suites for domain-specific use cases with composable test templates
4. Compare model performance across versions or providers to inform deployment decisions
5. Integrate evaluations into CI/CD pipelines for automated quality gates on model releases
TAGS
evaluationllmtestingbenchmarkingqualitysafetyai-evaluationopen-source