RAG (Retrieval-Augmented Generation), Explained

What RAG actually is - retrieving the right facts from your own data first, then asking the model to answer using them - why an LLM needs it, how the pipeline works end to end, and why good RAG is mostly good retrieval.

1. The Problem RAG Solves An LLM only knows its training data - stale, generic, and blind to your docs - so it fills the gaps by making things up. RAG fixes this by retrieving the relevant facts first and asking the model to answer using them: the open-book exam.
2. How RAG Works The RAG pipeline end to end: chunk your documents, embed the chunks into a vector store ahead of time, then at query time embed the question, retrieve the most relevant chunks, stuff them into the prompt as context, and generate the answer.
3. Why It's Harder Than It Looks RAG quality is retrieval quality. The failure modes - bad chunking, the model ignoring or misusing context, retrieving irrelevant or stale chunks, and thin context that still leaves it hallucinating - plus how to defend (cite sources, evaluate retrieval, keep the index fresh), and the clear line between RAG and fine-tuning.