Evaluating generative AI assistants before you ship them
Reliable generative AI assistants are built around an evaluation set: real questions with expected answers that are scored before every release. Combine automatic checks for grounding and citations with human review of a sample, and track cost and latency alongside quality.
Kabir Singh, Head of AI (M.S., Machine Learning)Updated 1 min read
Start with real questions
Pull questions from support tickets, search logs and interviews. Write the expected answer and the source document for each.
Measure what matters
- Correctness against the expected answer
- Grounding: is every claim supported by a retrieved source?
- Citation accuracy
- Refusal when the answer is not in the knowledge base
Keep humans in the loop
Automatic scoring scales, but a weekly human review of sampled conversations catches problems metrics miss.