What is Retrieval Augmented Generation (RAG)?

Retrieval Augmented Generation (RAG) is an AI architecture that combines large language models with external knowledge retrieval to generate more accurate, up-to-date, and verifiable responses.

How RAG Works

RAG operates in two phases: first, it retrieves relevant documents from a knowledge base using semantic search; then, it feeds these documents as context to an LLM to generate a grounded response. This approach reduces hallucinations and allows the model to access information beyond its training data.

Key Components of RAG

A RAG system consists of three main components: (1) a document store or vector database like Pinecone or Chroma, (2) an embedding model to convert text to vectors, and (3) a large language model for generation. The retriever finds relevant chunks, and the generator synthesizes them into coherent answers.

When to Use RAG

RAG is ideal for applications requiring current information, domain-specific knowledge, or verifiable sources. Common use cases include customer support chatbots, enterprise search, legal document analysis, and medical information systems.

Frequently Asked Questions

What is the difference between RAG and fine-tuning?

Fine-tuning permanently modifies model weights with new data, while RAG dynamically retrieves relevant information at inference time. RAG is better for frequently changing data; fine-tuning is better for teaching new skills or styles.

What vector databases work with RAG?

Popular vector databases for RAG include Pinecone, Weaviate, Chroma, Milvus, and pgvector. Cloud options include AWS OpenSearch and Azure AI Search.

How do I reduce RAG hallucinations?

Reduce hallucinations by improving retrieval quality with better chunking strategies, using reranking models, implementing citation verification, and instructing the LLM to only use provided context.