Buying Guide
RAG vs. Fine-Tuning
Both adapt a general model to your specific knowledge, but they solve different problems and are often confused as interchangeable.
Choose RAG — Your problem is 'answer questions using our current documents/data,' and that data changes regularly.
Choose Fine-tuning — You need the model to consistently produce a specific tone, format, or behavior — not to know new facts.
| What it's good for | RAG grounds answers in your current documents; fine-tuning changes the model's behavior/style, not its factual knowledge. |
| Keeping data current | RAG updates instantly when you update the source documents; fine-tuning requires retraining to reflect new information. |
| Cost and complexity | RAG is generally simpler and cheaper to set up and maintain than fine-tuning. |
| When fine-tuning wins | Fine-tuning is the right tool when you need a specific output format/tone/behavior consistently, not new factual knowledge. |
Cost implications
RAG's ongoing cost is mostly retrieval infrastructure (a vector store, embedding calls) that scales predictably with data volume. Fine-tuning has a real upfront training cost plus the ongoing cost of retraining every time you want the behavior to shift.
Migration considerations
Starting with RAG doesn't lock you out of fine-tuning later for tone/behavior — the two are commonly layered on top of each other, not swapped.
Hidden tradeoffs
- — RAG quality is capped by how well your documents are structured and chunked — bad source data means bad answers regardless of the model.
- — A fine-tuned model can drift out of sync with your current knowledge silently, since it isn't reading live documents.
Common mistakes
- — Fine-tuning a model hoping it will 'learn' company facts, then finding it still hallucinates or goes stale the moment the source documents change.
- — Assuming RAG and fine-tuning are mutually exclusive — many production systems use both, for different reasons.
When to choose neither
If the model's default behavior already answers correctly with a well-written system prompt, you may not need either — start with prompting before reaching for RAG or fine-tuning.
Decision checklist
- □Does the requirement change with new information, or is it a fixed behavior/style?
- □How current does the source data need to be at answer time?
- □Do you have clean, structured documents to retrieve from, or does that need to be built first?
Bottom line
For 'answer questions from our documents,' RAG is almost always the right starting point. Fine-tuning solves a different, narrower problem.
Have a project in mind?
Tell us what you're trying to automate or build — we'll reply with next steps, not a sales pitch.