braintrust-evals

LLM evaluation experiments compare models and prompts by applying scoring functions to datasets and tracking results. You define datasets with input and expected results, run configurations across model families, and monitor performance metrics in Braintrust.

Model benchmarking systematically evaluates LLM performance using scoring rubrics applied to evaluation datasets. Scoring functions measure specific output criteria, enabling structured comparisons across prompt variants and model families to identify weaknesses and iterate improvements.

Custom datasets with expected results are fully supported for LLM evaluation. You create datasets containing input-output pairs, apply them across experiments with different model configurations and scoring functions, and track comparisons to benchmark performance.

Evaluation dashboards diagnose LLM weaknesses by displaying structured outputs and scoring results from experiments. You review tracked comparisons across prompt variants, identify failure patterns in the results, and iterate prompts to improve model performance.

Automating end-to-end LLM evaluation involves orchestrating experiments, datasets, and scoring functions together. You define datasets, run experiments across model configurations, apply scoring rubrics, and track results with JSON schema compliance for structured outputs.

LLM evaluation supports structured outputs with JSON schema compliance. Experiments generate structured outputs from models, apply scoring functions to validate results against schemas, and track comparisons to ensure robust benchmarking across configurations.