7 types of AI evaluations every engineer should run, from functional to regression
goyalshaliniuk · x · 2026-10-13
A practical taxonomy of AI evaluation types for engineers: getting an app running isn't enough for production—you need systematic evals across quality, reliability, safety, and performance.
The seven types (with an accompanying infographic):
- Functional – correct outputs, tool use, workflow completion
- Factuality & groundedness – verify claims, citations, sources; reduce hallucinations
- Reasoning & task-completion – multi-step solving, planning, tool selection, final success rate; best for agents and coding assistants
- Safety & security – prompt injection, jailbreaks, sensitive data exposure, unauthorized tool actions
- Robustness – typos, ambiguous instructions, adversarial inputs
- Performance & cost – latency, token consumption, throughput, per-task cost
- Regression – compare against baselines, re-run fixed cases, catch failures before release
Build strategy: create a representative dataset → define success metrics per eval type → run on every model/prompt/tool/retrieval change → mix deterministic checks, model-based evaluators, and human review. The goal isn't proving the AI never fails, but understanding where and how often it fails and whether changes improve it.
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