Module 8 of 13 in the AI Track, a free, self-directed curriculum for building AI that moves revenue.
The idea
RAG is how you make a model answer from your facts instead of whatever it picked up in training. That’s the actual mechanism behind “every claim traces to a source.” Go deep here. Embeddings and a vector database, where you pick one and learn it cold. Chunking, retrieval, re-ranking, and hybrid search that mixes keywords with meaning.
Then the part amateurs skip. Grounding and citation, which means forcing the answer to come only from what was retrieved. The ways it breaks: bad retrieval, the model losing the fact buried in the middle, stale data. And how to test a RAG system for faithfulness, which loops right back to the evaluation work in Module 4.
Why it matters
This is the load-bearing wall under most commercial AI claims. Verified, cited output isn’t a feature you bolt on at the end. It’s RAG done properly. This is the module where a pitch turns into a product.
Build this
A RAG system over a set of real docs and case studies that answers questions with citations. It’s something you could sell, and it’s proof the no-hallucination claim is real rather than aspirational.
How to work through it
- DeepLearning.AI’s RAG and vector database courses
- Anthropic’s contextual retrieval post
- Pick one vector database and build the pipeline
- Add citation enforcement and a faithfulness check
Resources
- DeepLearning.AI’s short courses on RAG and vector databases
- Anthropic’s contextual retrieval
- Hugging Face’s NLP course