Pipevals

Pipevals is a tool that helps you build and manage the process of evaluating artificial intelligence models, specifically large language models or LLMs. It allows you to create visual pipelines, which are like flowcharts, to connect different steps involved in testing and scoring these AI models. You can link together AI model calls, ways for people to score the results, tools to change data, and automatic checks to measure performance. These pipelines can then be used on sets of data to see how the AI's quality changes over time.

Benefits

Pipevals makes it easy to see and build your evaluation process with a drag-and-drop interface. You can connect different parts of your pipeline, and the system helps manage how data flows between them. It supports using over 100 different AI models, allows you to change and organize data as needed, and captures important measurements to track performance. A key benefit is the ability to include human reviewers in the process, letting people score AI outputs based on clear guidelines. The system also keeps track of everything, so you can see how your AI models are performing over time.

Use Cases

This tool is useful for anyone building or using AI models who needs to check their quality. For example, you can use Pipevals to compare how well different AI models perform on the same task. It's also great for setting up a system where humans review and score AI generated content, ensuring it meets certain standards. You can track metrics to see if your AI is getting better or worse after updates. The visual nature of the tool makes it easy to understand complex evaluation setups.

Vibes

Pipevals is available on GitHub, suggesting it's a project that benefits from community input and development. While specific user reviews are not detailed, the project's availability and the features it offers point to its utility for developers and researchers working with LLMs.

Additional Information

Pipevals is built using PostgreSQL. To set it up, you need to have PostgreSQL installed and follow instructions to configure the tool by cloning the project from GitHub and setting up your environment variables. The project is licensed under an unspecified license.