Automated LLM Evaluation: Building a CI/CD quality gate that actually runs | Galtea Blog

Automated LLM evaluation offers a method for integrating quality checks into CI/CD pipelines by running evaluations against a versioned golden dataset whenever changes are made to prompts, model versions, or retrieval configurations. This approach differs from standard test automation by employing probabilistic rather than deterministic checks and incorporating the dataset as part of the system. The process ensures that quality regressions are identified before deployment by tracking trends, managing datasets actively, and setting dynamic thresholds to distinguish between genuine regressions and false alarms. Effective implementation requires version control, consistency in evaluation settings, and structured regression tracking to support proactive quality management in AI systems. Platforms like Galtea facilitate this process by enabling comprehensive evaluation pipelines aligned with formal product specifications, enhancing the ability to maintain and improve LLM performance over time.

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