RAG (Retrieval-Augmented Generation) is an AI architecture that enhances language models by combining them with external knowledge bases.

Instead of just relying on what the model learned during training, RAG lets the model pull in fresh, relevant information when responding to a query — making its answers more accurate, up-to-date, and trustworthy.

Why Use RAG?

• Improves accuracy & relevance: The model can reference reliable external sources, rather than guessing or hallucinating.
• Keeps knowledge fresh: Because it retrieves real-time data, RAG doesn’t rely solely on the static training corpus.
• Allows domain-specific responses: The architecture can tap private or organizational data (e.g. internal docs) to answer industry- or project-specific questions.
• Transparently cites sources: Many RAG systems include references or citations to the external texts they used, allowing users to verify the information.

How RAG Works - Process Overview

1. Query Received
   A user asks a question or provides a prompt.
2. Retrieval Phase
   The system searches an external knowledge source (documents, databases, knowledge bases) for relevant passages.
3. Augmentation / Context Enrichment
   The retrieved text is appended to the prompt or merged with it, so the language model has both the user’s query and the external info as context.
4. Generation Phase
   The LLM generates the answer, using both its own internal knowledge and the retrieved information.
5. (Optionally) Citation / Verification Output
   The generated response might include pointers or links to the sources it used, increasing transparency.

In Short: RAG (Retrieval-Augmented Generation) is an AI technique where a language model is augmented with external knowledge sources during inference. This enables the model to produce more accurate, up-to-date, and verifiable responses than a standalone model relying only on training data.