When you want AI that knows your company's products, writes in your brand voice, or follows your specific process — you have two main options: fine-tuning or RAG.
**Fine-Tuning**
You retrain the model on your custom dataset. The model's weights are updated so it 'internalizes' your data.
- ✅ Perfect for: Style, tone, format, specialized language
- ✅ Faster at inference (no retrieval step)
- ❌ Expensive to run repeatedly as data changes
- ❌ Prone to forgetting — may lose general knowledge
- ❌ Doesn't help with real-time or frequently updated data
- Best for: 'Write like our brand voice' or 'Follow this specific reasoning pattern'
**RAG (Retrieval-Augmented Generation)**
You embed your documents and retrieve relevant ones at query time.
- ✅ Always up-to-date (just re-index new docs)
- ✅ No model retraining needed
- ✅ Explainable — you can cite sources
- ❌ Slower (retrieval adds latency)
- ❌ Fails when retrieval fails
- Best for: 'Answer based on our documentation' or 'What does our policy say about X?'
**The real answer: use both.**
Fine-tune for behavior and style. Use RAG for knowledge. A model fine-tuned to be a helpful customer support agent + RAG over your product docs = the best of both worlds.
Cost reality check: RAG is almost always cheaper to maintain. Fine-tuning costs $100-$10,000+ depending on the model and dataset size, and needs to be redone every time your data changes significantly.
**Key takeaway:** Fine-tune for behavior/style. RAG for knowledge/facts. Best results combine both.