Evaluation starts with assembling a corpus of representative questions and expected answers (evaluation datasets).

Measure metrics like accuracy, semantic similarity and query performance.

Collect user feedback to identify misinterpretations and refine prompts or domain vocabulary.

Fine‑tuning a language model on your domain data can improve accuracy but requires caution to avoid overfitting or exposing sensitive data.

Alternatively, you can adopt prompt‑engineering techniques, such as few‑shot examples, to guide a general model.

Data teams should monitor drift as business terms evolve.

MageMetrics offers a feedback loop: when users correct or refine answers, the system learns associations between terms and metrics, gradually improving intent recognition.