Evaluating LLM output drift in CICD pipelines is vital for maintaining software reliability in applications powered by generative artificial intelligence. In traditional web development, unit tests and integration tests are deterministic: given the exact same input, a function either produces the expected output or fails the build.

Language models, however, are non-deterministic by design. Furthermore, cloud foundation model providers (such as OpenAI and Anthropic) silently update model weights, tune inference quantization, and adjust safety classifiers under existing model tags. A system prompt that successfully generated clean JSON yesterday may suddenly produce conversational markdown wrappers or truncate responses today.…