LLM evaluation: complete definition in AI for SMEs

LLM evaluation is the method of objectively measuring the quality, reliability and relevance of a language model's responses on test cases representative of your activity. It is not "testing if it works" — it is quantifying how well it works: accuracy, completeness, source fidelity, output format, response time, cost per query.

What it changes for an SME

LLM evaluation is what separates a we'll-see AI project from a steered one:

• Before deployment: comparing 2 models (GPT-4o vs Mistral) on your real use cases with your real data — choosing the one that performs best, not the one with the best reputation;
• During operation: detecting gradual response degradation (data drift, model changes by the vendor) before users complain;
• After a change: verifying that a prompt or model change has not broken 15% of responses.

Best practice

Evaluation is not a one-off exercise — it is a continuous process. You define a test set (20–50 real cases), run it regularly, compare results. In fractional AI leadership, we set up an evaluation dashboard that runs automatically and alerts when a quality threshold is crossed. It is the guardrail against silent model drift.