promptfoo-evaluation

Reproducible LLM evaluation requires structuring prompts, tests, and providers into a single configuration. You can standardize this process by applying rubric-based grading and custom assertions to generate consistent model-to-model comparison results.

Yes, Python assertions can be used for automated grading in LLM evaluations. This allows you to apply deterministic checks to model outputs, ensuring objective comparisons across multiple scenarios for QA or product validation.

Rubric-based grading evaluates LLM outputs against predefined structured criteria. It standardizes the comparison of prompts across different models by scoring responses based on specific test cases and rules, yielding reproducible benchmark results.

You configure test cases and providers by initializing a structured evaluation project. This involves loading your prompts and tests alongside your model providers to run comprehensive comparisons across various scenarios.

Custom assertions in LLM evaluation can utilize optional scripts or references to drive deterministic checks. The core requirement is a configuration file defining prompts and tests, while scripts enhance the evaluation logic.

Structured prompt evaluation pipelines are necessary when consistency and reproducibility across teams are critical. They replace manual testing by automating rubric-based grading and provider comparisons for robust QA, research, or product validation.