In this article
LLM evaluation (evals) encompasses offline evaluation (running fixed test datasets through the model + scoring), online evaluation (production quality monitoring), and human preference studies. Evaluation dimensions include: correctness/faithfulness (grounded in sources), relevance (answers the question), coherence (well-formed response), safety (no harmful content), and task completion. Common frameworks: RAGAS (for RAG pipelines), DeepEval, OpenAI Evals, and Braintrust. A key challenge is the 'eval gap' — no single automated metric fully captures human quality perception. Build eval suites before deploying new models or prompts.
What it means in practice
LLM Evaluation is not just vocabulary; it is a design handle. Across AI engineering and AI observability, this term connects implementation details with the bigger system decision being made. It becomes important after launch, when teams need evidence about quality, cost, regressions, and user-visible reliability.
Why engineers care
- It gives teams a shared name for the behaviour, risk, or architecture choice being discussed.
- It helps separate the goal from the implementation detail, so you can compare alternatives instead of copying a tool pattern blindly.
- It creates a useful checklist for reviews: inputs, outputs, failure modes, ownership, cost, latency, and measurement.
Production watch-outs
Avoid vanity metrics. Prefer measurements tied to user outcomes, regression prevention, incident response, and known quality risks.
Related context
Useful neighbouring concepts: Hallucination, Online Evaluation, Offline Evaluation, RAG.