Promptfoo: An AI Tool For Testing, Evaluating and Red-Teaming LLM apps

Promptfoo is a command-line interface (CLI) and library designed to enhance the evaluation and security of large language model (LLM) applications. It enables users to create robust prompts, model configurations, and retrieval-augmented generation (RAG) systems through use-case-specific benchmarks. This tool supports automated red teaming and penetration testing to ensure application security. Moreover, promptfoo accelerates evaluation processes with features like caching, concurrency, and live reloading while offering automated scoring through customizable metrics. Promptfoo is compatible with multiple platforms and APIs, including OpenAI, Anthropic, and HuggingFace, and seamlessly integrates into CI/CD workflows.

Promptfoo offers multiple advantages in prompt evaluation, prioritizing a developer-friendly experience with fast processing, live reloading, and caching. It is robust, adaptable, and effective in high-demand LLM applications serving millions. The tool’s simple, declarative approach allows users to define evaluations without complex coding or large notebooks. It promotes collaborative work with built-in sharing and a web viewer by supporting multiple programming languages. Moreover, Promptfoo is completely open-source, privacy-focused, and operates locally to ensure data security while allowing seamless, direct interactions with LLMs on the user’s machine.

Getting started with promptfoo involves a straightforward setup process. Initially, users have to run the command npx promptfoo@latest init which initializes a YAML configuration file, and then perform the following steps:

• Users need to open the YAML file and write a prompt they want to test. They should use double curly braces as placeholders for variables. 
• Add providers and specify the models they want to test. 
• Users need to add some example inputs to test the prompts. Optionally, one can add assertions to set output requirements that are checked automatically. 
• Finally, running the evaluation will test every prompt, model, and test case. When the evaluation is complete, outputs can be reviewed by opening the web viewer. 

In LLM evaluation, dataset quality directly impacts performance, making realistic input data essential. Promptfoo enables users to expand and diversify their datasets with the promptfoo generate dataset command, creating comprehensive test cases aligned with actual app inputs. To start, users should finalize their prompts, and then initiate dataset generation to combine existing prompts and test cases to produce unique evaluations. Promptfoo also allows customization during dataset generation, giving users the flexibility to tailor the process for varied evaluation scenarios, which enhances model robustness and evaluation accuracy.

Red teaming Retrieval-Augmented Generation (RAG) applications are essential to secure knowledge-based AI products, as these systems are vulnerable to several critical attack types. Promptfoo, an open-source tool for LLM red teaming, enables developers to identify vulnerabilities like prompt injection, where malicious inputs could trigger unauthorized actions or expose sensitive data. By incorporating prompt-injection strategies and plugins, promptfoo helps in detecting such attacks. It also solves the problem of data poisoning, where harmful information in the knowledge base can skew outputs. Moreover, for Context Window Overflow issues, promptfoo provides custom policies with plugins to safeguard response accuracy and integrity. The end result is a report that looks like this:

In conclusion, Promptfoo is a CLI and a versatile tool for evaluating, securing, and optimizing LLM applications. It enables developers to create robust prompts, integrate various LLM providers, and conduct automated evaluations through a user-friendly CLI. Its open-source design supports local execution for data privacy and offers collaboration features for teams. With dataset generation, promptfoo ensures test cases that align with real-world inputs. Moreover, it strengthens Retrieval-Augmented Generation (RAG) applications against attacks like prompt injection and data poisoning by detecting vulnerabilities. Through custom policies and plugins, promptfoo safeguards LLM outputs, making it a comprehensive solution for secure LLM deployment.

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