RAG, Explained Without the Hype
What retrieval-augmented generation actually is, when it beats fine-tuning, and where it quietly fails.
Retrieval-augmented generation is the workhorse pattern behind most useful LLM products. The idea is simple; doing it well is not.
How it works
Instead of hoping the model memorized your data, you retrieve relevant chunks at query time and put them in the prompt. The model answers from facts you supplied, with sources you can cite.
Why teams choose it
RAG keeps answers grounded and current, update the knowledge base and the answers change, no retraining. It's cheaper and more controllable than fine-tuning for factual recall.
Where it fails
Bad retrieval means bad answers. Most RAG problems are retrieval problems: poor chunking, weak embeddings, or no reranking. Fix retrieval before blaming the model.
