Marvin introduces a new concept called AI Functions. These look and feel just like regular Python functions: you provide typed inputs, outputs, and docstrings. However, instead of relying on traditional source code, AI functions use LLMs like GPT-4 as a sort of “runtime” to generate outputs on-demand, based on the provided inputs and other details. The results are then parsed and converted back into native data types.

This “functional prompt engineering” means you can seamlessly integrate AI functions with your existing codebase. You can chain them together with other functions to form sophisticated, AI-enabled pipelines. They’re particularly useful for tasks that are simple to describe yet challenging to code, such as entity extraction, semantic scraping, complex filtering, template-based data generation, and categorization. For example, you could extract terms from a contract as JSON, scrape websites for quotes that support an idea, or build a list of questions from a customer support request. All of these would yield structured data that you could immediately start to process.

We initially created Marvin to tackle broad internal use cases in customer service and knowledge synthesis. AI Functions are just a piece of that, but have proven to be even more effective than we anticipated, and have quickly become one of our favorite features! We’re eager for you to try them out for yourself.

We’d love to hear your thoughts, feedback, and any creative ways you could use Marvin in your own projects. Let’s discuss in the comments!