Aishwarya Naresh Reganti (ML Researcher) presents a hands-on roadmap to dive into the innovative world of Retrieval Augmented Generation (RAG) for Large Language Models (LLMs). This guide is crafted from the best free resources available and designed to take you through the process of understanding, building, and evaluating your own RAG application in just 3 days. - Day 1: Introduction to RAG Learn the what, why, and how of RAG, including key components like Ingestion, Retrieval, and Synthesis, alongside pipeline components such as Chunking and Indexing. - Day 2: Advanced RAG + Building Your Own RAG System Explore advanced optimizations and build your RAG system using LangChain and OpenAI. Delve into resources for building sophisticated applications. - Day 3: RAG Evaluation and Challenges Understand evaluation metrics from TruEra and RAGas, and navigate through common RAG pain points and solutions. Optional Reading Resources & Top 2024 RAG Research Papers: Stay updated with the latest in RAG research and further your understanding beyond the basics. Spend 2-3 hours daily on this roadmap to step confidently into the evolving landscape of RAG and its applications.