RAG Introduction: What Is Retrieval Augmented Generation?

Part of the RAG series.

RAG stands for Retrieval Augmented Generation. It is a pattern that combines two things: retrieval (fetching relevant information from your own data) and generation (using a language model to produce an answer). The model does not rely only on what it was trained on; it is "augmented" at run time with retrieved documents. That is why RAG is so useful for knowledge bases, internal docs and domain-specific Q&A: you get answers that are grounded in your own content and that can cite sources.

Why RAG Matters

Plain language models are limited by their training cut-off and by what fits in the context window. They also hallucinate when asked about topics they are unsure about. RAG attacks these problems by:

Letting the model use up-to-date or private data (your docs, tickets, wikis) without retraining.
Providing explicit sources so users can verify answers.
Reducing hallucinations by constraining the model to talk mainly about the retrieved text.

How RAG Works in One Paragraph

The user asks a question. You convert that question (and sometimes your documents) into vector embeddings and run a similarity search over a vector store to get the most relevant chunks. You then pass those chunks plus the question to the language model as context. The model generates an answer using mostly that context. Optionally you add retrieval scoring, reranking, or hybrid search to improve which chunks get selected.

The Basic RAG Flow

A minimal RAG pipeline has three stages:

Indexing. Documents are split into chunks, converted to embeddings and stored in a vector database (or another search backend).
Retrieval. For each query, you embed the query, search for the top-k similar chunks and optionally rerank them.
Generation. You build a prompt with the question and the retrieved chunks, send it to an LLM and return the generated answer (and optionally the source chunks).

In code, a minimal retrieval step could look like this (conceptually):

# Pseudocode: retrieve top-k chunks for a query
query_embedding = embed(query)
results = vector_db.similarity_search(query_embedding, k=5)
context = "\n\n".join([chunk.text for chunk in results])
prompt = f"Answer based only on:\n{context}\n\nQuestion: {query}"
answer = llm.generate(prompt)

RAG vs Other Approaches

RAG is often compared to fine-tuning and to using a single big context window:

RAG vs fine-tuning. Fine-tuning teaches the model new knowledge or style using examples; RAG feeds the model external documents at answer time. RAG is easier to update (change the index, not the model) and fits fast-changing or proprietary data. Fine-tuning is better when you need consistent format, tone or ingrained behaviour.
RAG vs "stuff everything in context". You could dump all docs into one prompt, but that hits context limits and cost, and hurts focus. RAG retrieves only what is relevant, so you can scale to large corpora and keep latency and cost under control.

What You Need to Build RAG

To build a RAG system you need:

An embedding model to turn text (queries and passages) into vectors.
A vector store (or search engine with vector support) to store and search those vectors.
A language model (API or self-hosted) to generate answers from query + retrieved context.
A chunking strategy so that documents are split in a way that keeps meaning intact and fits your retrieval and context window.

From there you can add rerankers, hybrid search, caching and evaluation. We cover those in the rest of the RAG series.

Where to Go Next

This post is the parent for our RAG series. For more detail on each part of the stack, see:

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