# RAG vs Fine-Tuning: A Practical Decision Framework
One of the most common questions in applied AI: should you use Retrieval-Augmented Generation (RAG) or fine-tune a model? The answer, as always, is "it depends." But here's a framework to help you decide.
When to Use RAG
- Your knowledge base changes frequently — RAG pulls from a live index, so updates are immediate
- You need source attribution — RAG naturally provides references to source documents
- You have limited training data — RAG works with as few as a handful of documents
- Accuracy on facts matters most — RAG grounds the model in retrieved context
When to Fine-Tune
- You need a specific style or format — Fine-tuning teaches the model HOW to respond
- Latency is critical — No retrieval step means faster responses
- You have abundant labeled data — Fine-tuning shines with thousands of examples
- Cost per query matters — Shorter prompts (no retrieved context) = fewer tokens
The Hybrid Approach
In practice, we use both. Our pipeline fine-tunes a model for style and format consistency, then augments it with RAG for factual grounding. This gives us the best of both worlds.
Decision Matrix
| Factor | RAG | Fine-Tuning | |--------|-----|-------------| | Setup time | Hours | Days-Weeks | | Data freshness | Real-time | Snapshot | | Cost per query | Higher (more tokens) | Lower | | Style control | Limited | Strong | | Factual accuracy | Higher | Model-dependent |
Choose based on your constraints, not industry hype.