"It's Hard to Eval" Is a Product Smell (hamel.dev)

A recent article addresses a prevalent issue in AI product development: the challenge of verifiability, which is often dismissed as “our product is hard to eval.” The author argues that this objection is a significant product weakness, as processes difficult for developers to evaluate will likely be challenging for users as well. To effectively tackle this issue, the design of a product should prioritize ease of verification before developing evaluation tools, using examples from AI data agents, lesson plan generators, and medical report tools to illustrate best practices. These examples reveal that enhancing user trust hinges on providing transparent, verifiable outputs rather than opaque answers. For instance, an AI data agent could present not just a net revenue figure but also a breakdown of how that figure was calculated, including insight into the data sources used and validation processes. Such transparency not only eases the verification burden for users but also simplifies automated evaluations of the AI's performance. By integrating these design principles, products can become more user-centric and evaluation processes can yield more reliable metrics, ultimately enhancing the overall quality and effectiveness of AI tools in various domains.