Promptfoo is an innovative, developer-friendly tool designed to streamline the development and testing of Large Language Model (LLM) applications.
It offers a comprehensive suite of features to evaluate, secure, and optimize LLMs, helping developers transition from a trial-and-error approach to a more structured and reliable development process.
Key Features Of Promptfoo
- Automated Evaluations: Promptfoo allows developers to test their prompts and models through automated evaluations, providing insights into how well their LLMs perform under various conditions.
- Red Teaming and Vulnerability Scanning: It includes robust red teaming capabilities to identify vulnerabilities in LLM applications, ensuring they are secure against potential threats.
- Model Comparison: Developers can compare different models side-by-side, including popular options like OpenAI, Anthropic, Azure, Bedrock, and Ollama, to choose the best fit for their applications.
- CI/CD Integration: Promptfoo supports automation in Continuous Integration/Continuous Deployment (CI/CD) pipelines, streamlining the development process.
- Collaboration Tools: Results can be easily shared with team members, facilitating collaboration and decision-making.
Benefits Of Using Promptfoo
- Developer-First Approach: Promptfoo is designed with developers in mind, offering features like live reload and caching for faster development.
- Privacy: It runs entirely locally, ensuring that sensitive prompts never leave the developer’s machine.
- Flexibility: Compatible with any LLM API and programming language, making it versatile for various development environments.
- Battle-Tested: Proven to power LLM applications serving over 10 million users in production.
- Data-Driven Decisions: Provides metrics-based insights to guide development decisions.
- Open Source: Licensed under MIT, with an active community contributing to its development.
Getting Started With Promptfoo
To begin using Promptfoo, developers can install and initialize the project using npx promptfoo@latest init
, followed by running their first evaluation with npx promptfoo eval
.
The tool offers comprehensive documentation and guides for both evaluations and red teaming, making it accessible for developers to dive in and start optimizing their LLM applications.