RAG stands for Retrieval-Augmented Generation. The idea fits in one sentence: instead of asking a language model to answer purely from what it learned during training, you first hand it the relevant documents to read, then ask it to answer based on them. This guide is the entry point to the more specific articles on this blog about the topic.

The problem RAG solves

A trained LLM has knowledge frozen at a given date and knows nothing about your private data,your internal documents, your customer base, your product docs. Two options to close that gap: fine-tuning, retraining the model on your data (expensive, slow, needs redoing on every update), or RAG, handing it the relevant documents at question time (fast to update, no retraining).

The 4 steps of a RAG pipeline

• ✓1. Ingestion: split your documents into reasonably sized chunks and turn them into numerical vectors (embeddings) that capture their meaning.
• ✓2. Storage: store these vectors in a vector database (Qdrant, Pinecone, pgvector) that can retrieve the closest ones to a given query.
• ✓3. Retrieval: at question time, turn the question itself into a vector and find the semantically closest chunks.
• ✓4. Generation: inject those chunks into the prompt sent to the LLM, along with the question, and ask it to answer based only on that context.

# Simplified RAG pipeline step
question = "What is the refund policy?"
question_vector = embed(question)

relevant_chunks = vector_db.search(question_vector, top_k=4)

prompt = f"""Answer the question based only on this context.

Context:
{chr(10).join(relevant_chunks)}

Question: {question}
"""

answer = llm.generate(prompt)

What RAG does not solve

RAG does not make a model smarter, it just gives it access to more information. If the retrieved chunks are off-topic (bad chunking, bad embeddings), the LLM generates a confident but wrong answer,a hallucination built on bad context is still a hallucination.

When to use RAG

• ✓Your data changes often (product catalog, documentation, support knowledge base).
• ✓You need to cite the sources behind an answer,traceability is native to RAG.
• ✓You want to avoid the cost and latency of retraining on every content update.

Going further

This guide covers the foundations. To go deeper: choosing an embedding model, choosing a vector database, RAG architecture in production, optimizing chunking, or comparing RAG with fine-tuning and prompt engineering,each topic has its own detailed article on this blog.