Evaluating Generative AI Systems

A production AI evaluation should answer: Does the system solve the intended problem reliably, safely, and economically?

Evaluate multiple layers

Retrieval

For RAG systems, measure whether the required evidence is retrieved. Useful measures can include recall-oriented retrieval metrics and task-specific relevance judgments.

Generation

Check correctness, groundedness, completeness, format compliance, and refusal behavior against a representative evaluation set.

Safety

Test harmful requests, prompt attacks, sensitive information handling, policy boundaries, and unsafe tool actions.

Operations

Measure latency, error rate, throughput, token usage, cost per request, and dependency failures.

Evaluation loop

Dataset → baseline → automated evaluation → human review → error analysis → change → regression test

Do not optimize only for a benchmark score. A system can improve on a benchmark while becoming more expensive, slower, less safe, or worse on your real workload.

Connected knowledge

RAG hallucinations → embedding mismatch → production RAG → architecture interview.