What is a RAG agent and when should a business use one?

RAG stands for Retrieval-Augmented Generation. A RAG agent retrieves relevant chunks from a vector database of your documents, then passes those chunks to an LLM (like GPT-4 or Claude) as context before generating an answer. The result: precise, verifiable responses grounded in your actual data rather than the model's potentially outdated or hallucinated training knowledge.

The clearest sign a business needs a RAG agent is when employees spend time searching internal documents, policy manuals, or knowledge bases to answer questions that should have a definitive answer. We built Meghdoot for the Ministry of External Affairs precisely for this reason — IFS diplomatic officers needed answers drawn exclusively from verified government sources, not general web knowledge. The RAG pipeline ensures every response is traceable to a specific government document.

Other strong use cases include: customer support bots that must answer only from your product documentation, HR assistants that reference current company policies, and compliance tools that must cite the exact clause in a regulation. If your use case requires "answer from our data only," RAG is the right architecture.