LLM Evals

The foundation of reliable AI products — evals are unit tests for AI

Functional, quality, safety, and regression evals

Golden datasets, synthetic data, production sampling

LLM-as-judge, assertions, semantic similarity scoring

When and how to involve human reviewers effectively

promptfoo, Braintrust, LangSmith, Humanloop, custom solutions

Catch quality drops before they ship to production

Write the eval first, then improve the prompt — TDD for AI

Running evals in your deployment pipeline — blocking deploys on regression

See eval patterns in action with live code

Common eval mistakes and how to avoid them

Production-ready eval guidelines

Tools, blogs, papers, and guides

Testing JSON conformance, schema validation, and reliable structured generation

Reading LMSYS, MMLU, SWE-bench, and domain evals correctly

Probing for jailbreaks, prompt injection, and safety failures