Build an End-to-End RAG Pipeline for LLM Applications

This article was originally written by Shaoni Mukherjee (Technical Writer) Large language models have transformed the way we build intelligent applications. Generative AI Models can summarize documents, generate code, and answer complex questions. However, they still face a major limitation: they cannot access private or continuously changing knowledge unless that information is incorporated into their training data. Retrieval-Augmented Generation (RAG) addresses this limitation by combining information retrieval systems with generative AI models. Instead of relying entirely on the knowledge embedded in model weights, a RAG system retrieves relevant information from external sources and provides it to the language model during inference. The model then generates a response grounded in this retrieved context. An end-to-end RAG pipeline refers to the full system that manages this process from beginning to end. It includes ingesting documents, transforming them into embeddings, storing th