Introduction
Promptfoo is an open-source framework purpose-built for evaluating, testing, and benchmarking LLM prompts, allowing teams to manage prompt variations systematically and optimize them with confidence. Promptfoo brings test-driven development to prompt engineering by offering automated comparison, scoring, and regression testing for LLM outputs. Promptfoo is used to validate prompt quality across multiple model providers, benchmark prompt variants under controlled conditions, and enforce guardrails around safety, relevance, and grounding in production applications.
Key benefits of using promptfoo include:
Prompt Benchmarking Across Models: Easily compare prompts across different LLMs (e.g., OpenAI, Anthropic, Hugging Face, vLLM) and configurations to find the best-performing option.
Test-Driven Prompt Development: Define test cases with expected outputs or scoring criteria to catch regressions and enforce consistency over time.
Flexible Scoring Methods: Supports both human-readable evaluations (e.g., thumbs up/down, similarity scores) and automated metrics like BLEU, ROUGE, and embedding similarity.
Custom Evaluators and Assertions: Enables teams to build custom logic for evaluating factuality, tone, formatting, safety, or Cake-specific quality heuristics.
CLI and CI Integration: Works seamlessly with GitHub Actions and other CI systems, allowing prompt tests to run automatically on every pull request or model update.
Promptfoo is used to manage and improve prompts for AI copilots, document summarization, multi-turn agents, and internal query answering tools. It helps ensure that updates to prompts or model backends do not silently degrade performance, and supports fast, safe iteration by embedding prompt testing into the development workflow. By adopting promptfoo, you can ensure its LLM-powered systems are consistent, measurable, and continuously improving—bringing engineering rigor to the art of prompt design.