Most LLM applications are a lot more complex than just prompts.

For example, a RAG-based chatbot might have the following setup.

• classify user query intent
• (LLM) normalize the query
• (ApiCall) call some APIs based on user query intent
• (Retrieval) retrieve relevant documents from Vector DB
• (LLM) generate a response based on the info in the previous steps

Athina has always allowed you to monitor and trace complex chains like this.

But now... we are also allowing you to build and prototype chains like this dynamically in a spreadsheet-like UI 🚀🚀

How does it work?

• You can add dynamic column to run prompts on an LLM, call an API endpoint, extract structured data, classify values, retrieve documents, etc
• You can add as many dynamic columns as you would like to build up a complex data pipeline

Here's a 30 second demo showing you how a dynamic column works.