RAG Step by Step
A model does not know your college rules or today's news. RAG lets it read the right pages of your documents before it answers. In this lesson you follow one question through the whole pipeline.
New words
- RAG
- Retrieval-Augmented Generation: find useful text first, then let the model answer from it.
- Indexing
- Preparing your documents once so they can be searched fast.
- Retrieval
- Finding the chunks that are closest in meaning to the question.
- Top-k
- The k best-matching chunks. For example, top 3 means the best 3.
- Hallucination
- A confident answer from the model that is false or made up.
- Citation
- A note that tells the reader which source the answer came from.
What you will learn
- The problem: the model has not read your documents
- Two phases: indexing and answering
- Phase 1 in detail: build the index
- Phase 2 in detail: answer a question
- Try it: follow one question
- RAG or fine-tuning?
- Common failures
- In a real system
- Interview tip
- Key takeaways
10 quiz questions at the end.
The full lesson, with interactive demos and the quiz, opens after you sign in with Google. It is free.