RAG retrieves relevant documents at runtime — cheap, flexible, real-time updates. Fine-tuning trains a model on your data — expensive, static, but higher quality for specific patterns. Here's when to use each.
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| Factor | RAG | Fine-Tuning |
|---|---|---|
| How it works | Retrieves documents and adds to prompt | Retrains model weights on your data |
| Setup cost | $500-5,000 | $5,000-50,000 |
| Update speed | Instant (just add documents) | Weeks (requires retraining) |
| Per-query cost | Higher (more tokens) | Lower (knowledge is in model) |
| Accuracy | Good (depends on retrieval) | Excellent for trained patterns |
| Flexibility | High (any document) | Low (only what it was trained on) |
| Data privacy | Documents stay on your server | Data sent to model provider for training |
| Maintenance | Low (update document store) | High (retrain when data changes) |
When to Use RAG
- Knowledge changes frequently (product catalogs, policies, news)
- You need citations and source attribution
- You have large document sets (thousands of pages)
- Budget is under $5,000 for setup
- You need to control what information the AI can access
- Multiple use cases from the same document set
Example: Customer support bot that answers from your help docs, FAQ, and knowledge base. When you update a policy, the bot knows immediately.
When to Use Fine-Tuning
- Consistent patterns the model needs to learn (tone, format, style)
- Domain-specific language or jargon
- Small, stable dataset that doesn't change often
- You need maximum quality for a specific task
- Latency matters (RAG adds retrieval time)
- You've already tried RAG and quality isn't good enough
Example: A medical AI trained to always respond in a specific clinical format, using specific medical terminology, with consistent risk assessments.
Why Most Businesses Should Start with RAG
- 10x cheaper to set up
- Instant updates — no retraining needed
- No data leakage — your documents stay private
- Transparent — you can see which documents the AI used
- Flexible — works with any LLM, switch models anytime
Start with RAG. Only fine-tune if RAG doesn't meet your quality needs. 90% of business AI use cases work great with RAG alone.
Choose: RAG or fine-tuning
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