AI evals are a data science problem: What most teams get wrong

Hamel Husain emphasizes the crucial role of data science in AI engineering, particularly in evaluating and improving AI systems, as discussed in his talk at Arize Observe 2026. He highlights that despite AI applications showing green metrics, underlying issues often persist in production, necessitating a return to data science practices to solve evaluation problems effectively. The workflow he suggests involves developers and PMs using traces for debugging and quality judgment, focusing on failure modes rather than generic metrics, and validating evaluations with human labels to ensure reliability. He criticizes the current trend of relying on superficial metrics and the improper use of LLMs for evaluation without rigorous validation, advocating for a disciplined approach similar to classifier validation with labeled datasets and performance tracking. Husain argues that AI product teams need to integrate data science methodologies into their processes to define and maintain quality, suggesting that the evaluation loop should involve thorough error analysis, human-in-the-loop validation, and evidence-based decision-making to enhance AI system performance.

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