RAG is a technique that combines retrieval-based and generative AI models to produce highly contextual domain-specific responses, with applications in Question & Answering, Summarization, Report Generation, and more. A simple conversational RAG application can be built using Symbl.ai's Nebula LLM as the generative model, Milvus Vector DB for storing conversation data vectors, MPNet V2 Embedding Model from Hugging Face to derive embeddings, and LangChain as an orchestration framework. To implement this, developers need to install required libraries, load API keys, initiate vector databases and embedding models, create a sample conversation, import the Nebula LLM, create prompt templates, and chain conversations using the LangChain framework. This approach allows for high-quality output from domain-specific models like Nebula, trained on interaction data, which can produce accurate vector embeddings on conversational data and knowledge.