AI Evals (Evaluation)

AI Evals (Evaluation) refers to the systematic assessment of AI system outputs using predefined test cases, metrics, and scoring criteria. Evals range from automated unit tests that check exact output format to human preference studies that measure overall response quality. They are the engineering foundation for iterative improvement of LLM-powered products.

How it works

An eval suite consists of input examples, expected outputs or evaluation criteria, and a scorer. Scorers may be exact match, regex, code execution (for programming tasks), embedding similarity, or an LLM judge. Evals are run against a baseline and a candidate system; performance deltas determine whether a change is an improvement. Continuous eval runs in CI/CD pipelines catch prompt or model regressions automatically.

Key facts

• Frameworks: OpenAI Evals, Braintrust, Promptfoo, Langfuse, and Inspect (UK AISI) are widely used eval platforms.
• Eval types: Functional correctness, safety, factual accuracy, format compliance, latency, and cost are common dimensions.
• Dataset contamination: Public eval benchmarks may be present in model training data, inflating scores artificially.
• Human baselines: Calibrating automated evals against human ratings ensures they measure what users actually care about.

For builders

Building an eval suite before shipping an AI feature is the single highest-leverage quality investment for product teams. Evals enable confident model upgrades, catch prompt regressions, and provide objective evidence for prioritization decisions. Start with a small curated set of 50 to 200 golden examples covering known edge cases, then grow the suite as production logs reveal new failure modes.

Sources

• Chen, M., et al. (2021). Evaluating Large Language Models Trained on Code (HumanEval). arXiv:2107.03374. arxiv.org
• Jimenez, C., et al. (2023). SWE-bench: Can Language Models Resolve Real-World GitHub Issues? arXiv:2310.06770. arxiv.org
• Hendrycks, D., et al. (2020). Measuring Massive Multitask Language Understanding (MMLU). arXiv:2009.03300. arxiv.org
• Stanford CRFM. Holistic Evaluation of Language Models (HELM). crfm.stanford.edu
• Zheng, L., et al. (2023). Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena. arXiv:2306.05685. arxiv.org