Answers
RAG or fine-tuning for a company knowledge base?
RAG, for nearly every business use case. It stays current without retraining, and every answer can cite its source — which matters for trust. Fine-tuning is worth it mainly when you need consistent style or format, not factual recall, and increasingly the two work together rather than as a strict either/or.
Fine-tuning bakes facts into the model's weights at a point in time — the moment anything changes, that knowledge is stale until you retrain, which costs real money and real engineering time every cycle. Retrieval-augmented generation instead pulls current facts from a live source at answer time, so the model never needs to "know" your latest pricing or policy — it just needs to find and cite it.
The trust argument matters as much as the cost one. A RAG answer can point to the exact document it drew from, which lets someone verify a claim in seconds. A fine-tuned answer has no such trail — you're trusting the model's memory of training data it saw once, with no way to check it against a source.
Fine-tuning does earn its place, just for a different job: teaching a model to consistently write in your voice, follow a specific output format, or handle a narrow classification task well. Increasingly the two aren't an either/or — fine-tune for tone and structure, then use RAG to inject the facts that actually need to stay current.