Why AI Product Evaluation Is Hard and How to Build a Scientific Assessment Framework
The article analyzes the unique challenges of evaluating AI products—output uncertainty, subjective criteria, over‑fitting risk, high cost, and vague metrics—compares traditional testing with AI testing, proposes a five‑step evaluation workflow, defines concrete metrics such as pass rate and efficiency gain, and illustrates the process with a real‑world sales‑script generation case study, concluding with five key success factors and future trends.
