What is RAG?
Retrieval-augmented generation gives an LLM relevant information at query time instead of relying on what it memorized in training. You search your own documents for passages related to the question, put them in the prompt, and ask the model to answer from them. It is how you make an LLM answer about your data.
Why it matters
LLMs do not know your private data and hallucinate on specifics. RAG is the dominant pattern for building useful LLM applications — chatbots over your docs, support assistants, internal search — without retraining. It is one of the most in-demand AI engineering skills right now.
What to learn
- Embeddings and semantic similarity
- Chunking documents sensibly
- Vector search and retrieving relevant passages
- Building the prompt from retrieved context
- Citing sources in the answer
- Evaluating retrieval quality
- The retrieve-then-generate pipeline
Common pitfall
Blaming the LLM when answers are wrong, while the real problem is retrieval — the relevant passage was never found, so the model had nothing to work from. RAG quality is mostly retrieval quality. Measure whether the right chunks are being retrieved before tuning the generation prompt.
Resources
Primary (free):
- LangChain — RAG tutorial · docs
- Pinecone — RAG guide · docs
- Hugging Face — RAG · docs
Practice
Build a minimal RAG pipeline: chunk a few documents, embed them, store the vectors, retrieve the top matches for a question, and put them in the prompt for an answer with citations. Ask something only your docs contain. Done when wrong answers can be traced to retrieval versus generation.
Outcomes
- Explain embeddings and semantic retrieval.
- Chunk documents and build a retrieve-then-generate pipeline.
- Cite retrieved sources in the answer.
- Diagnose whether retrieval or generation caused a bad answer.