| RAG (Retrieval-Augmented Generation) | VS | Fine-Tuning |
|---|---|---|
| Injects relevant documents into the LLM prompt at query time for up-to-date, grounded answers. | Trains the model on domain-specific data to permanently embed knowledge and behavior. |
RAG (Retrieval-Augmented Generation)
Pros
- No model retraining needed
- Always up-to-date
- Transparent source citations
- Lower compute cost
Cons
- Retrieval quality affects output
- Vector DB setup required
- Prompt length limits
Fine-Tuning
Pros
- Deep domain expertise
- Smaller prompts, faster inference
- Custom tone and style
- No retrieval latency
Cons
- Expensive training compute
- Can hallucinate if data is poor
- Requires periodic retraining
- Data preparation is complex
Verdict
RAG is faster to implement and keeps data fresh. Fine-tuning gives deeper customization but costs more. Most teams start with RAG and fine-tune only when retrieval isn't enough.
When to use which
Use RAG for knowledge bases, FAQ systems, and dynamic content. Use fine-tuning for specialized domains, custom output formats, and when latency matters.
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