AI Development2026-04-10 · 8 min

AI Evaluation: How to Measure Quality Before Launch

Accuracy is not enough. Here are the metrics that predict whether an AI system works in production.

Start with the business metric

Define what good looks like in business terms first: deflection rate, resolution time, error reduction. Model metrics only matter if they predict these.

Build a golden set

Collect 50–200 real queries with verified ideal answers. This becomes your regression suite — run it on every prompt or model change.

Measure retrieval and generation separately

For RAG systems, retrieval quality (recall@k, precision) and answer quality (faithfulness, helpfulness) fail differently and need separate fixes.

Automate evaluation

LLM-as-judge and pairwise comparison can scale evaluation, but keep a human-reviewed core set. Continuous evaluation after launch catches drift early.

Frequently Asked Questions

What is LLM-as-judge?

Using a strong model to score another model's outputs against rubrics. It is a fast, scalable proxy for human review — but it needs calibration on your domain.

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