Automates LLM output testing and prompt optimization using the Promptfoo framework for systematic benchmarking.
This skill integrates the Promptfoo framework into your development workflow, enabling systematic evaluation of LLM performance across various models and prompts. It provides structured guidance for creating configuration files, implementing custom Python assertions, and utilizing LLM-as-judge rubrics to ensure high-quality, consistent AI outputs. By streamlining the benchmarking process, this skill helps developers manage complex few-shot examples, perform regression testing on prompts, and make data-driven decisions about model selection and prompt engineering strategies.
主要功能
01LLM-as-judge (llm-rubric) implementation for qualitative output scoring
020 GitHub stars
03Cost-free preview mode using the Echo provider to verify prompt rendering
04Automated prompt benchmarking across multiple providers like Anthropic and OpenAI
05Custom Python assertion support for specialized metric and logic validation
06Comprehensive few-shot example management for complex prompt patterns
使用场景
01Validating long-form content generation using custom text reduction and accuracy metrics
02Regression testing prompts to ensure updates improve performance without breaking existing logic
03Comparing output quality and latency between different model versions for specific use cases
