RAG combines retrieval from your knowledge base with LLM generation. This helps produce answers grounded in your own data.
flowchart LR
A[User question] --> B[Retriever]
B --> C[Vector DB / Docs]
C --> B
B --> D[Top relevant chunks]
D --> E[LLM]
A --> E
E --> F[Grounded answer]
Why teams use RAG
Injects current enterprise knowledge without model retraining.
Improves factuality and traceability.
Supports source-backed answers.
Typical RAG pipeline
Ingest docs and split into chunks.
Create embeddings and store in vector DB.
At query time, retrieve top chunks.
Compose prompt with question + chunks.
Generate answer and optionally cite sources.
Example
Internal HR bot answers "How many casual leaves do I get?" using your policy docs, not public internet text.