In Chip Huyen’s AI Engineering, Chapter 4 focuses on the critical aspect of evaluating AI systems, emphasizing three components: evaluation criteria, model selection, and building evaluation pipelines. Evaluation-driven development helps define how applications will be assessed before resource investment. Companies should establish specific criteria tailored to their applications, often requiring varied models for different tasks. The chapter discusses potential pitfalls, such as the fragility of multiple-choice question scoring and the inadequacy of traditional metrics like fluency and coherence for modern models. It highlights the importance of factual consistency, safety, and instruction-following capabilities. Companies face a trade-off between developing internal models and choosing commercial alternatives, with significant implications for performance and resource investment. Moreover, the chapter stresses the need for ongoing evaluation and adaptation of evaluation pipelines to align with business objectives. Overall, a robust evaluation process is essential for creating reliable and effective AI systems.
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