Evaluation metric example: RAG document relevance

AI evaluation in n8n

This is a template for n8n's [evaluation feature](https://docs.n8n.io/advanced-ai/evaluations/overview).

Evaluation is a technique for getting confidence that your AI workflow performs reliably, by running a test dataset containing different inputs through the workflow.

By calculating a metric (score) for each input, you can see where the workflow is performing well and where it isn't.

How it works

This template shows how to calculate a workflow evaluation metric: retrieved document relevance (i.e. whether the information retrieved from a vector store is relevant to the question).

The workflow takes a question and checks whether the information retrieved to answer it is relevant.

To run this workflow, you need to insert documents into a vector data store, so that they can be retrieved by the agent to answer questions. You can do this by running the top part of the workflow once.