What is RAG (Retrieval-Augmented Generation)?
Last updated May 2026A technique that lets AI access external knowledge bases to provide more accurate answers.
Definition
Retrieval-Augmented Generation (RAG) is an AI architecture that combines a language model with a retrieval system. Instead of relying solely on training data, RAG retrieves relevant documents from an external knowledge base and includes them in the prompt context. This produces more accurate, up-to-date, and verifiable responses while reducing hallucinations.
💡 Example
A company chatbot using RAG would first search the company knowledge base for relevant documents, then feed those documents to an LLM along with the user question. The LLM generates an answer grounded in the actual company data.
Related concepts
A type of AI trained on massive text datasets to understand and generate human language.
When an AI model generates plausible-sounding but factually incorrect information.
A numerical representation of text that captures its meaning as a vector.
Why this matters
RAG (Retrieval-Augmented Generation) is how AI tools give accurate, source-based answers instead of relying on potentially outdated training data. It is the most practical enterprise AI architecture — cheaper than fine-tuning and more accurate than raw prompting.
Real-world example
Perplexity uses RAG: when you ask a question, it first searches the web for relevant sources, then generates an answer grounded in those sources. NotebookLM uses RAG with your uploaded documents. This is why RAG-based tools hallucinate less than pure chatbots.
See it in action
A database optimized for storing and searching AI embeddings at scale.
Explore AI tools
Find tools that use rag (retrieval-augmented generation) in practice.
What is RAG (Retrieval-Augmented Generation)?
Retrieval-Augmented Generation (RAG) is an AI architecture that combines a language model with a retrieval system. Instead of relying solely on training data, RAG retrieves relevant documents from an external knowledge base and includes them in the prompt context. This produces more accurate, up-to-date, and verifiable responses while reducing hallucinations.
How does RAG (Retrieval-Augmented Generation) work in practice?
A company chatbot using RAG would first search the company knowledge base for relevant documents, then feed those documents to an LLM along with the user question. The LLM generates an answer grounded in the actual company data.
How does RAG compare to fine-tuning for adding custom knowledge?
RAG retrieves relevant information at query time from an external knowledge base, making it easy to update and requiring no model training. Fine-tuning bakes knowledge into the model weights, requiring retraining when information changes. RAG is better for factual knowledge; fine-tuning is better for changing model behavior.
What components are needed to build a RAG system?
A basic RAG system requires a document corpus, an embedding model to convert text into vectors, a vector database for storing and searching embeddings, and a language model to generate answers. Tools like LangChain, LlamaIndex, and cloud providers offer frameworks that simplify RAG implementation.
What are common problems with RAG implementations?
Common issues include retrieving irrelevant documents due to poor chunking or embedding quality, the model ignoring retrieved context in favor of its training data, context window limits when too many documents are retrieved, and maintaining the knowledge base as source documents change.