Why traditional CI/CD fails for LLMs (and the release gates we built to fix it)
The article discusses the inadequacy of traditional CI/CD pipelines for deploying large language models (LLMs) in production. It argues that standard release gates, designed for deterministic software, fail to handle the non-deterministic and behaviorally complex nature of LLMs. The author shares a practical release-gating approach built to address these challenges, focusing on evaluating model outputs, safety, and performance before release. The approach includes gates for prompt robustness, output quality, and safety checks. The consequence is that teams must adopt specialized validation methods to prevent issues like hallucinations, bias, or security vulnerabilities in production AI systems.
Developers need new release gates to ensure LLM reliability and safety in production.