Elevating your RAG System: A step-by-step guide to advanced enhancements via LLM evaluation, with a real-world data use case
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This article will guide you through building an advanced Retrieval-Augmented Generation (RAG) pipeline using the llama-index framework.
A Retrieval-Augmented Generation (RAG) system is a framework that makes generative AI models more accurate and reliable by using information from outside sources. In the context of this project, legal documents will be used as the external knowledge base.
In this tutorial, we’ll start by establishing a basic RAG system before illustrating how to include advanced features. One of the challenges in constructing such a system is deciding on the best components for the pipeline. We will attempt to answer this by evaluating the critical components of the pipeline.
This article serves as a practical tutorial for implementing RAG systems, including their evaluation. While it doesn’t delve deeply into theoretical aspects, it will explain the concepts used in this article as thoroughly as possible.