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The Evaluation Guidebook is an open educational resource created by Hugging Face that explains how to evaluate machine learning and large language models effectively. It compiles practical insights and theoretical knowledge gathered from real-world evaluation work, including experience managing the Open LLM Leaderboard and designing evaluation tools. The guidebook teaches developers how to design evaluation pipelines, select appropriate metrics, and interpret model performance results. It discusses multiple evaluation strategies, ranging from automated benchmarks to human evaluation and LLM-based evaluation techniques. The material also highlights the strengths and weaknesses of different evaluation methods, helping practitioners understand when and how to apply them. By organizing evaluation knowledge into structured sections, the project helps engineers and researchers build more reliable and trustworthy AI systems.
Features
- Guidelines for evaluating large language models and AI systems
- Practical tutorials on designing custom evaluation pipelines
- Explanations of evaluation metrics and benchmarking strategies
- Insights from real-world LLM evaluation and leaderboard management
- Coverage of automated, human, and hybrid evaluation methods
- Best practices for interpreting model performance and limitations
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