LLM Evals (Large Language Model Evaluations)

An eval suite is built from a representative set of real or realistic test cases — actual customer questions, edge cases, adversarial attempts to break the agent — each paired with a known-good answer or a rubric describing what a good answer looks like. The model or agent's actual output is then scored against that rubric, either by an automated grading model (an 'LLM-as-judge'), a rules-based checker, or human reviewers, producing a pass rate or quality score. Evals are run before launch to catch failures, and continuously afterward so a prompt change, model upgrade, or new document in the knowledge base doesn't silently degrade quality — this is what separates a demo from a production system.

For an Arabic-first deployment, evals matter more than in English-only markets because dialect and formality failures are easy to miss in a quick demo but obvious to a real customer: before taking a voice agent live for a Jeddah client, we run its transcripts against a Hijazi-dialect test set and check for correct handling of code-switching (Arabic mixed with English brand or product names), ensuring the pass rate clears an agreed threshold before go-live, and we re-run the same suite after any change to the prompt or underlying model.