Building a Robust RAG System with Langfuse and RAGAS: A Complete Implementation Guide with Python

Learn how to create, monitor and evaluate your RAG system using Langfuse observability and RAGAS metrics

You’ve all heard of RAG systems. With the development of AI, there are of course developments in this area. RAG systems combine the power of access-based methods with generative AI models, offering improved accuracy, reduced hallucinations, and better auditability compared to pure LLM solutions.

But there’s a point we shouldn’t miss. Let’s say you’ve built a great system and want to take it live. How do you know if your system is receiving the right information? Are the responses produced by LLM true to the content received? And how can you measure the overall performance of the system and improve it over time?

This is where observability becomes very important. In this article, we will cover the steps of setting up a full RAG system with Langfuse observability integration and RAGAS evaluation metrics. While Langfuse allows us to monitor all stages of our system, RAGAS metrics allow us to measure the quality of our responses. By combining these two powerful tools we will create not only a functioning RAG system but also one that can be continuously improved…