Let's tune RAG pipelines with Fondant#
Retrieval Augmented Generation (RAG) has quickly become the go-to architecture for providing large language models (LLM) with specific knowledge. Optimizing a custom setup requires days to find the right set of parameters and system configuration.
We have created an example use case to show how you can enhance your RAG setup by using Fondant. Checkout out the resources:
Off-the-shelf solutions might be easy to set up for a quick proof of concept, but their performance is usually insufficient for production usage since they are not adapted to the complexities of specific situations.
By employing several different methods in an iterative manner, we can more than double the accuracy of an RAG system and maximize its performance. We have built two Fondant pipelines that can assist you in finding the optimal combinations of parameters for your unique setup.
In the example repository, you can find notebooks that will help you customize your setup. More information can be found in the blog post.