Shaik Abdul Kareem

Introduction In today’s information-driven age, data is a critical resource for businesses, researchers, and individuals. However, this data often exists in silos—fragmented across systems, unstructured, and inaccessible for effective analysis. The challenge is not merely having vast amounts of data but making sense of it in meaningful ways. Enter Retrieval-Augmented Generation (RAG), a technique that combines the strengths of information retrieval and natural language generation to extract and synthesize knowledge. RAG systems retrieve relevant data