Minimum Viable Benchmark (For Evaluating LLMs) (blog.nilenso.com)

A recent discussion among AI engineering leaders emphasized the inadequacies of popular public benchmarks for evaluating AI model suitability for specific products. The conversation highlighted that while benchmarks can serve various roles—such as decision-making tools, improvement indicators, and research agenda setters—they often fall short by oversimplifying complex model performance into single scores. The concern is that relying solely on these metrics can mislead teams, particularly when different benchmarks evaluate distinct attributes, as illustrated by comparisons in the legal domain where a model could excel in one benchmark but perform poorly in another. To address these challenges, the concept of a "minimum viable benchmark" (MVB) was introduced, advocating for internal benchmarks tailored to specific tasks and business goals. This approach allows teams to collect relevant data, assess AI performance in real-world scenarios, and identify meaningful metrics without the pressure to compete against established public benchmarks. A well-structured MVB can provide valuable insights into a product's effectiveness, ultimately leading to more effective AI deployments and continuous improvement over time. This strategy encourages AI practitioners to move away from overreliance on external benchmarks and focus on internal evaluation methods that align closely with their specific use cases.