Two of the most common questions in any AI project: should we use retrieval-augmented generation (RAG) or fine-tune a model? Having shipped both across a dozen projects, here’s how we decide — and the real-world trade-offs.
What each approach does
RAG retrieves relevant context from your data at query time and feeds it to a general model. Fine-tuning trains a model on your examples so the behaviour is baked in.
When RAG wins
- Your knowledge changes often — docs, policies, catalogs, prices
- You need source citations and up-to-date answers
- You want to start fast and cheap, with no training pipeline
When fine-tuning wins
- You need a specific tone, format, or structured output every time
- Latency and per-token cost matter at scale (shorter prompts)
- The task is narrow and stable
The numbers
In our projects, RAG typically reaches usable accuracy in days, with most of the cost in retrieval infrastructure. Fine-tuning takes longer to set up but can cut prompt size and per-call cost by 40–70% for high-volume, repetitive tasks. Often the best answer is both — RAG for fresh knowledge, light fine-tuning for consistent format.
Our rule of thumb
Start with RAG. Add fine-tuning only once you have real usage data showing it pays off in accuracy, latency, or cost.
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