What is LLM testing? How AI testing works

LLM testing combines conventional software verification with evaluation of variable model outputs, since deterministic application components such as prompts, parsers, schemas, retrieval pipelines, tool wrappers, and orchestration can use fixed assertions, while response quality requires scored criteria for correctness, relevance, tone, safety, and other behaviors. A layered testing strategy includes unit, integration, end-to-end, behavioral, regression, performance, and security testing, with inexpensive deterministic checks run frequently and more costly model evaluations used during reviews and before release. Effective evaluation relies on versioned datasets, clear rubrics, code-based and model-based scorers, human review, and repeated trials to account for non-determinism, provider drift, and multiple acceptable answers. Regression testing compares prompts, models, and retrieval configurations against stable baselines using aggregate quality floors and per-case limits, while performance testing tracks latency, concurrency, reliability, and token costs. Security testing uses adversarial cases for prompt injection, jailbreaks, data leakage, and unauthorized tool use, retaining discovered vulnerabilities as permanent regression tests. In CI/CD, teams can run fast tests and sampled evaluations on pull requests, apply full quality, security, and performance gates before release, then use staged rollouts and production scoring to detect new failures and convert production traces into future test cases; Braintrust is presented as a platform for managing these datasets, scorers, experiments, traces, and release criteria.

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