What happened: RAG (retrieval-augmented generation) is the pattern where a model looks up relevant documents from your own data before answering, instead of relying only on what it memorized during training. Think open-book exam, not closed-book recall.
Why it matters: Models don't know about your internal wiki, your latest pricing sheet, or yesterday's support ticket. RAG bridges that gap without retraining the model — you keep a general-purpose model and just feed it the right context at question time.
When you need it
- Answering questions about private or fast-changing data (docs, tickets, policies)
- Reducing hallucination by grounding answers in real source text
- Citing sources — RAG naturally supports "here's where this came from"
When you don't
If the task is pure reasoning, creative writing, or code generation with no private data involved, RAG adds latency and complexity for no benefit. Don't reach for it by default.
Common pitfalls
Bad chunking (splitting documents in ways that break context), retrieving irrelevant passages that confuse the model, and skipping evaluation — "it looks right" isn't the same as measuring retrieval quality.
Originally published on ayraix.com, practical AI for enterprise builders.
