Comparison
RAG vs Fine-Tuning
When to retrieve, when to retrain.
Overview
Both techniques specialize an LLM for a domain, but they optimize for different constraints. RAG injects fresh knowledge at query time; fine-tuning bakes patterns into model weights.
Differences
| Dimension | Option A | Option B |
|---|---|---|
| Freshness | Updated by re-indexing | Stale until retrained |
| Cost | Low ongoing | High per training run |
| Best for | Facts, docs, citations | Tone, format, style |
| Setup time | Hours | Days |
Use Cases
- →Customer support over a changing knowledge base → RAG
- →Consistent brand voice across all generations → Fine-tuning
Recommendation
Start with RAG. Only fine-tune once you have evidence that prompt + retrieval cannot produce the behavior you need.
Related Workflows
Related Tool Stacks
↳ connected nodes