AI judges AI output quality with LLM-as-a-Judge

The article highlights a fundamental shift in software quality assurance as generative AI becomes more common in business. Unlike traditional software, where inputs yield predictable outputs, generative AI's outputs vary with probability, making simple correctness checks insufficient. To address this, dotData proposes a structured approach where humans define what constitutes good output, and an evaluation AI applies those criteria consistently.

The three essential elements—evaluation rubric, evaluation dataset, and evaluation AI—work together. The rubric clarifies what output should achieve, the dataset includes good and bad examples, and the evaluation AI scores outputs with reasons. dotData notes that while building a prototype achieving about 80 points is relatively easy, maintaining quality through model updates is a challenge.

A practical example shows how scores are assigned: for a retail data analysis, "instant total gross profit" scores 3 points (Perfect), "gross margin" scores 2 points (Good), and "unnecessary tax" scores 0 points (Failure). This demonstrates that even with clear criteria, interpretation matters, and human review of the scores and reasons remains essential.