RAG is the most important pattern for building real AI products. It lets an LLM answer questions about your documents — company docs, a textbook, a codebase — without retraining, and with far fewer hallucinations.
The problem RAG solves
An LLM only knows its training data (frozen, generic, no access to your private/recent info). Ask it about your company's policy and it guesses. RAG fixes this by retrieving the relevant text and handing it to the model at question time.
The pipeline
INDEXING (once): documents → split into chunks → embed each chunk → store vectors in a DB ANSWERING (per question): 1. embed the user's question 2. find the most similar chunks in the vector DB (retrieval) 3. stuff those chunks + the question into the prompt 4. LLM answers using ONLY that provided context (generation)
The prompt that makes it work
Context:
"""{retrieved_chunks}"""
Answer the question using ONLY the context above.
If the answer is not in the context, say "I don't know".
Question: {user_question}Why it's everywhere
- Grounded — answers cite real sources; hallucination drops sharply.
- Fresh & private — update the docs, not the model. Keeps data in your control.
- Cheap — no fine-tuning; just retrieval + a normal API call.
"Chat with your PDF", customer-support bots, internal knowledge assistants — nearly all are RAG. Build one in Build a RAG Chatbot.