The RAG Revolution | RAG vs Fine Tune | Agentic RAG | ReAct Framework

Description

These concepts collectively provide a comprehensive overview of Retrieval-Augmented Generation (RAG), contrasting it with fine-tuning and exploring its practical implementation and evaluation. The technical documents explain the RAG architecture, detailing stages like pre-retrieval optimization (handling noise and low information density), hierarchical indexing, and post-retrieval reranking of chunks. Furthermore, the sources discuss the metrics used for RAG evaluation, such as Hit Rate, MRR, and RAGAS components (groundedness, relevance, and faithfulness), noting that ground-truth metrics correlate better with human judgment than reference-free approaches. Finally, the texts cover the real-world application of RAG in enterprise settings, presenting a case study on its usability for internal data retrieval and a tutorial on building an AI agent with RAG, tools, and an interactive user interface using frameworks like Flask and the Gemini API.