When Building AI Apps, User Adoption Is Everything — And Rigorous Evaluation Is the Key to It

In my discussions with Enterprise users and the broader AI community, I’ve noticed that many people use the terms “AI Benchmarks” and “Evaluations” as if they mean the same thing. However, these terms actually refer to quite different concepts—there is a significant distinction between AI Benchmarks and Evaluations.

AI Benchmarks are like standardized exams for AI models, using fixed datasets and metrics to objectively compare different foundation models. They are essential for establishing industry standards and leaderboards but may not fully reflect how a model performs in real-world scenarios.

Here is an example of an AI Benchmark. In this example you can see a benchmark for Computer Science and Programming topic and specific sub-topic – MBPP Plus. This benchmark was run against several models listed below and a leader board was established for Accuracy.

AI Evaluations encompass a broader set of activities aimed at assessing an AI model’s performance, reliability, and suitability for specific tasks or Industry and Enterprise specific use cases. Evaluations can include robustness testing, and multidimensional metrics to provide a more comprehensive understanding of an AI system’s fit for your use case. Here is an example of AI Evaluations. There is a manufacturing company that has created an AI app to help non-technical users get access to critical data by just using natural language. The AI app they built converts the natural language requests to SQL queries, fetches data from various databases and presents it to the users. This enterprise wants to ensure accuracy and correctness of this AI app. They perform Evaluations to determine that.

Here is a table that summarizes it.

In summary, benchmarks are standardized tools for comparison of AI models, while evaluations are comprehensive processes for assessing real-world readiness and effectiveness of AI applications for your specific use case. Both are crucial, but they serve different roles in the development and deployment of AI systems.