Retrieval-augmented generation gives a language model relevant context at query time instead of relying only on what it memorized during training. It is the most reliable way to make a model answer questions about your private data.
The basic loop
Retrieve relevant chunks, stuff them into the prompt, and let the model answer using that grounded context. Quality lives and dies on the retrieval step.
Common pitfalls
- Chunks that are too large dilute relevance; too small lose meaning.
- Skipping evaluation means you never know if retrieval is actually working.
- Forgetting to cite sources erodes user trust.
Start simple, measure retrieval quality, and only add complexity when the metrics tell you to.