build-eval-dataset

Creating evaluation datasets for LLM applications requires a structured approach focusing on dataset representativeness, label quality, and iterative building. You need human input for reference answers and structured dataset management to ensure accurate performance metrics.

Sourcing data for LLM evaluation datasets involves acquiring it from real production traffic, beta usage, expert input, and LLM-generated cases. This ensures your dataset represents real usage and covers hard cases essential for reliable LLM app evaluations.

Structuring LLM evaluation datasets involves developing them in layers: smoke, core, and regression. This layered approach covers different aspects of LLM app performance, ensuring comprehensive evaluation across various usage scenarios.

Your LLM evaluation dataset fails to catch known regressions because it lacks proper verification and clear, versioned, representative labels. Focusing on label quality and iterative dataset building ensures the dataset catches known regressions and provides a baseline for continuous improvement.

Yes, you need human input to build datasets for LLM app evaluations. The dataset creation process specifically requires human input for reference answers to maintain label quality and ensure the dataset provides a reliable baseline for continuous improvement.