Lesson 7 of 7
Putting RAG to work
4 min read
You've got embeddings, chunks, retrieval, and a vector database. So what's the one idea that makes it all hang together?
What you've got now
In a handful of short lessons you've assembled the whole retrieval stack — not as vendor magic, but as steps you can reason about and tune on any "chat with your data" system that lands on your desk.
- —RAG — retrieve relevant passages, then generate an answer grounded in them.
- —Embeddings — text as points in space, where nearness means similar meaning.
- —The pipeline — chunk → embed → retrieve top-k → generate.
- —Vector databases — store embeddings and find nearest neighbors fast, with metadata filters.
- —Semantic vs hybrid, RAG vs fine-tuning — match the tool to the job: meaning plus keywords, knowledge vs behavior.
The one principle to keep
If you remember one thing: ground the answer. Everything in RAG serves a single goal — put the right passages in front of the model so it answers from real sources instead of fuzzy memory. Retrieval quality is the whole game; the generator is only as good as what you feed it.
When a RAG answer is wrong, debug retrieval first. Nine times out of ten the model answered fine from bad or missing chunks — fix what it's fed before you touch the prompt.
A RAG answer comes back wrong. Following this course's one principle, where do you look first?
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