What RAG solves
Retrieval-Augmented Generation helps an AI system answer from relevant external information. Instead of asking the model to remember everything, the application retrieves useful context and provides it to the model.
Typical flow
A query can be embedded, compared with stored vectors, and used to retrieve relevant chunks. Those chunks are then supplied as context for generation.
Why it matters
RAG is useful when the source of truth changes frequently or belongs to a specific application, team or organization. It can make answers more grounded and easier to update.
Implementation mindset
Good retrieval, clean source data, sensible chunking and access controls are critical. RAG is not simply a database plus an LLM; retrieval quality directly affects answer quality.