What is RAG? Retrieval-Augmented Generation Explained
RAG is the bridge between powerful language models and your private data. It's the most deployed pattern in production AI.
Quick Answer
RAG (Retrieval-Augmented Generation) enhances LLM responses by first retrieving relevant documents from a knowledge base, then using them as context for generation. This gives the LLM access to current, domain-specific information without fine-tuning.
Why RAG Matters
LLMs have a knowledge cutoff and can hallucinate. RAG solves both problems by grounding responses in your actual data. It's cheaper and faster than fine-tuning.
The RAG Pipeline
Indexing: chunk documents → generate embeddings → store in vector DB. Retrieval: convert query to embedding → find similar chunks. Generation: pass retrieved chunks + query to LLM → get grounded answer.
Use Cases
- Enterprise knowledge bases and documentation search
- Customer support with product-specific answers
- Medical or legal research assistants
- Internal company Q&A systems
When Not to Use
- General knowledge questions the LLM already handles well
- Tasks requiring real-time data (use function calling instead)
- When your documents are too short to benefit from retrieval
Build this properly → Start the LangChain Course
Go from concepts to production-ready AI applications with our structured, hands-on course.
Start the Course