Quality can be subjective, so use a mix of automated metrics and human feedback. Tools like BLEU or ROUGE scores help measure consistency, but they don’t catch everything.
Build simple review dashboards where testers or domain experts can rate outputs on accuracy, relevance, and tone. Track and analyze mistakes or user flags to see patterns you can fix in training.
Make sure your evaluation sets stay updated as your use cases evolve. Combining automation and human review ensures your AI stays reliable and useful over time.
