Introduction to RAG

Relax. We'll talk through this in plain words — no textbook voice.

What you'll walk away with

Understand Introduction to RAG without any of the intimidation
Get hands-on practice trying it yourself
Learn to spot — and smile past — the easy-to-make mistakes

Learn how Retrieval-Augmented Generation finds relevant information in your own documents and uses it to ground the LLM's answers.

Let's think about it this way for a second

In RAG, you first find the document chunks relevant to the user's question, then feed that context into the prompt and ask the model to answer from it. It's not retraining the model — it's handing it the information it needs at answer time.

Introduction to RAG lesson illustration — RAG and Vector Search — Introduction to RAG

Let's connect this to everyday life

RAG is great for source-grounded answers like company policy, product docs, and knowledge-base Q&A. If retrieval is bad, generation will be bad too. Don't just check answer quality — check the retrieved chunks as well.

Clean and chunk the documents
Generate embeddings and store them in an index
Find the chunks relevant to the question
Have the model answer with context and citations

Let's try it hands-on together

text

Documents → Chunk → Embed → Vector store
                                  ↑
Question → Embed → Search ────────┘
                    ↓
          Context + Question → LLM → Answer

You should see

You'll be able to walk through the RAG pipeline step by step.

5-minute try-it

Sketch out a RAG flow for five FAQ documents. Add a rule so that when retrieval finds nothing, it replies "couldn't find that information" instead.

A quick word of caution

Don't treat AI output as the final word. Have a human review anything important — including code and user data — before it's actually used.

OpenAI — Retrieval — OpenAI

Easy traps

Assuming RAG completely eliminates hallucination
Checking only the final answer, without showing sources

Exercise

Sketch out a RAG flow for five FAQ documents. Add a rule so that when retrieval finds nothing, it replies "couldn't find that information" instead.

You'll know it worked when: You'll be able to walk through the RAG pipeline step by step.