In this blog, we will dive deep into the Retrieval –Augmented Generation (RAG) concept and explore how it can be used to enhance the capabilities of language models. We will also build an end –to–end application using these concepts. Let’s understand about R AG is, its use cases, and its benefits. Retrieval–augmented generation (RAG) is a process of optimizing the output of a large language model so it references an authoritative knowledge base outside of its training data source before generating a response. In –shot RAG is a technique for enhancing the accuracy and reliability of generating an AI model with facts fetched from external sources. I will explain how to create a RAG application to query your own PDF. For this, we will leverage aws bedrock Llama 3 8B Instruct model, LangChain framework and streamlit. Key Technologies 1. Streamlit: a. Interactive front–end for the application.b. Simple yet powerful framework for building Python webapps. 2. LangChain: a. Framework...