Hamel Husain explains why AI evals fail before the evaluation begins

Hamel Husain discusses the common pitfalls in AI evaluations, emphasizing that many evaluations are flawed from the start due to poor product design and ambiguous evaluation criteria. He highlights that teams often misattribute weak outputs solely to model failures without considering that the product might not have gathered the necessary context or clearly defined evaluation standards. Husain stresses the importance of beginning evaluations with robust product design, clear trace inspection, and domain expert involvement, particularly in addressing issues like query disambiguation. Evaluation criteria should evolve as products are tested in real-world scenarios, with developers treating these criteria as versioned artifacts to track changes and understand their impact. Generic AI evaluation metrics often fail to capture critical failures, underscoring the need for error analysis to identify significant patterns and improve diagnostic value. Husain advocates for a better interface for reviewing agent traces, allowing domain experts to swiftly and effectively contribute to error analysis, which is crucial for refining AI evaluation processes.

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