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Definition·
LLM

RAG (Retrieval-Augmented Generation)

RAG is a technique that combines an LLM with a search engine over your own documents: the model retrieves the relevant information first, then generates an answer grounded in that information.

Detailed explanation

Retrieval-Augmented Generation injects your own knowledge into an LLM without retraining it. Workflow: (1) your documents are split into chunks and turned into embeddings stored in a vector database; (2) at query time, the most relevant chunks are retrieved; (3) those chunks are added to the prompt sent to the LLM, which generates an answer grounded in your data. RAG drastically reduces hallucinations, allows source citation, and keeps knowledge up to date without re-training. It is the most common architecture for enterprise AI assistants.

Examples

Internal assistant that answers HR or IT questions from company documentation
Legal chatbot that cites the exact articles of law
Technical support tool searching the product knowledge base

Frequently asked questions

What is the difference between RAG and fine-tuning?

RAG injects knowledge at query time without retraining. Fine-tuning changes the model itself to adopt a style or behavior. They are complementary.

Which vector database should I choose for RAG?

pgvector (PostgreSQL) for simplicity, Qdrant or Weaviate for scale, Pinecone for fully managed. Choose based on your existing stack.

Related terms

Vector Database

A database specialized in storing and searching embeddings by semantic similarity.

Embedding

An embedding is a numerical vector representation of text, image, or audio data that captures its semantic meaning, enabling similarity search and machine learning on unstructured content.

Hallucination

An AI response that sounds credible but is factually wrong or fabricated.

LLM (Large Language Model)

An LLM is a large language model trained on massive text corpora, capable of understanding and generating natural language across many tasks: writing, summarization, translation, code, reasoning.

Prompt Engineering

Prompt engineering is the discipline of designing, structuring, and refining the instructions given to an LLM to obtain reliable, accurate, and well-formatted answers.

Last updated: 7/15/2026

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