What RAG Means
RAG stands for Retrieval-Augmented Generation. It’s a technique in AI and NLP that combines two powerful components:
R → Retrieval
The system searches and retrieves relevant information from external sources (like a database, knowledge base, or documents).
Example: Instead of relying only on what the model “remembers,” it fetches fresh, factual data from a knowledge store.
A → Augmented
The retrieved information is then augmented (added) to the model’s context.
This step enriches the model’s input so it can generate answers that are more accurate, grounded, and up-to-date.
G → Generation
Finally, the model uses the retrieved and augmented context to generate a response.
This ensures the output is not just fluent but also factually supported by external knowledge.
