LLM Evaluation Fundamentals: Our Guide for Engineering Teams

Evaluating Large Language Models (LLMs) presents unique challenges compared to traditional software testing, mainly due to their probabilistic nature and the need for assessing outputs based on subjective criteria such as helpfulness, safety, and clarity. Unlike deterministic systems, LLMs require a holistic evaluation approach that evolves from simple "vibe checks" to comprehensive testing ecosystems, balancing both human and automated inputs. Evaluation involves defining quality amidst competing demands and utilizing traces for observability, enabling detailed analysis of LLM behavior. Offline and online evaluation strategies complement each other, with offline tests providing quick insights during development and online evaluations capturing real-world performance metrics like model drift and edge cases. Effective evaluation combines human judgment with automated assessments, aligning with specific application and safety goals. Platforms like PromptLayer facilitate structured evaluations by integrating trace data with human-in-the-loop and automated signals, promoting reliable LLM features through continuous testing and iteration.