RAG is revolutionizing natural language processing by combining retrieval-based and generative models, offering highly contextual domain-specific responses. This approach overcomes limitations of traditional generative models, which rely solely on training data, by incorporating external information from a large corpus. RAG has various use-cases in question answering, document summarization, chatbots, data analysis, content generation, and specific industries like customer support, healthcare, finance, legal, retail, and e-commerce. To implement RAG, users need to build a vector database of transcripts using Nebula Embedding API, generate response from Nebula LLM, and apply data safety measures such as data isolation, access control, encryption, audit trails, and data masking. Various vector databases like Weaviate, Milvus, Pinecone, Vespa.ai, Chroma, Nomic Atlas, and Faiss can be used for RAG implementation, with some offering free open-source options and others providing managed services or cloud-based solutions.