LLM Evaluation

Key facts

• Evals are the primary method by which researchers, engineers, and product teams compare models, track capability regressions, measure safety properties, and make deployment decisions.
• They range from automated benchmarks graded by code (MMLU, HumanEval, MATH, GPQA, SWE-bench) to human-preference comparisons (Chatbot Arena) to adversarial red-teaming for safety and robustness.
• For practitioners building LLM-powered products, custom evals are often more valuable than published benchmarks.
• A custom eval suite captures the actual query distribution, the output quality dimensions that matter (accuracy, format adherence, tone, latency), and the failure modes specific to the application — including edge cases that public benchmarks do not cover.
• Frameworks such as OpenAI Evals, Braintrust, LangSmith, and Inspect help teams build, version, and continuously track evaluation results.
• Key eval design considerations include: choosing between reference-based scoring (exact match, ROUGE, F1) and model-graded scoring (LLM-as-judge), preventing benchmark contamination from training data leakage, ensuring reproducibility via deterministic prompts and fixed temperature settings, and weighting edge cases rather than only the central distribution.
• As enterprise AI deployments mature, evals are increasingly required for compliance, safety sign-off, and vendor selection — making them as important for buyers as for model researchers.