RAG Explained: Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) is an AI architecture that retrieves relevant information from an approved knowledge source and provides that context to an LLM before the final response is generated.
Retrieval-Augmented Generation (RAG) is an AI architecture that improves the accuracy of Large Language Models (LLMs) by retrieving relevant data from external sources and providing it as context. RAG ensures that AI systems are grounded in enterprise facts, reducing hallucinations and enabling real-time data access.
RAG bridges the gap between the general knowledge of a model and the private, up-to-date data required by a business.
How it works
- Ingestion: Documents are broken into "chunks," converted into numerical embeddings, and stored in a vector database.
- Retrieval: When a user submits a query, the system searches the vector database for the most semantically relevant chunks.
- Augmentation: The retrieved context is injected into the LLM prompt alongside the original query.
- Generation: The LLM generates a response grounded in the provided factual context.
What is RAG?
Retrieval-Augmented Generation (RAG) is an AI framework that retrieves relevant information from a company’s private or updated data and provides it to a Large Language Model (LLM) as context. This ensures the AI’s answers are grounded in facts rather than just the general knowledge it was trained on.
How does RAG make AI smarter?
A RAG application retrieves relevant information from an approved knowledge source and provides that context to an LLM before the final response is generated. This reduces hallucinations by ensuring the model has the "open book" facts it needs to answer accurately.
Why RAG Matters
RAG dramatically reduces "hallucinations" (instances where the AI invents facts) by forcing the model to cite specific evidence from retrieved documents. This is essential for enterprise AI, where accuracy is non-negotiable.