4: Evaluating AI Systems and Building Evaluation Pipelines

This episode outlines crucial considerations for evaluating AI systems, emphasizing that a model's value is tied to its specific application. It discusses key evaluation criteria like domain-specific and generation capabilities (including factual consistency and safety), instruction-following, and also important practical aspects such as cost and latency. The piece also examines the complex decision of whether to self-host open-source models or utilize commercial model APIs, detailing the pros and cons based on factors like data privacy, performance, and control. Finally, it guides the reader through designing a robust evaluation pipeline, stressing the need for clear guidelines, relevant data, and continuous iteration, while acknowledging the limitations and potential data contamination risks of relying solely on public benchmarks.

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