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RAG
RAG vs Fine-tuning
Definition
RAG retrieves external knowledge at inference time while fine-tuning bakes knowledge into model weights during training - each approach suits different use cases for adding custom knowledge to LLMs.
Why It Matters
“Should I use RAG or fine-tuning?” is one of the most common questions in AI engineering. The answer depends on your specific requirements around knowledge freshness, accuracy needs, cost constraints, and the nature of your data.
When to Use RAG
- Knowledge changes frequently
- You need citations/sources
- You have large document collections
- Factual accuracy is critical
- You want to quickly add new information
- Budget is limited
When to Use Fine-tuning
- You need to change model behavior/style
- Knowledge is stable and well-defined
- You need faster inference (no retrieval)
- You want to teach domain-specific patterns
- Retrieval latency is unacceptable
Common Pattern: Both
Many production systems use both: fine-tuning for style, tone, and format adaptation; RAG for factual knowledge. This combines the benefits of both approaches while mitigating their individual weaknesses.