Fine-Tuning vs RAG
Two different approaches to making a general-purpose LLM behave like a specialist in your domain: fine-tuning retrains the model's weights on your data, while RAG (retrieval-augmented generation) leaves the model unchanged and feeds it relevant context retrieved at query time.
What It Is
Fine-tuning bakes knowledge or style into the model itself, the change is permanent until the model is retrained again.
RAG keeps the base model general and supplies facts from an external source, often a vector database, at the moment of the query, so responses reflect current data without any retraining.
The two are not competing techniques so much as tools suited to different problems: fine-tuning changes how a model behaves; RAG changes what it knows at the moment it answers.
In B2B Commerce Context
In B2B commerce, a support agent answering questions about pricing, stock levels, and account terms is a poor fit for fine-tuning, since the model would go stale the moment any of that data changes.
A practical example
Fine-tuning is a better fit for teaching a model a consistent tone, output format, or specialized task structure that doesn’t change day to day, many production systems use both together.
When You Need It
- Choose RAG when data changes frequently, you need traceability back to a source, or you want to avoid retraining costs.
- Choose fine-tuning when you need a consistent output format or style, the task is stable, or domain vocabulary is specialized enough that general retrieval doesn't capture it well.
- Consider combining both when you need consistent behavior and current facts.
Revisit the choice whenever the underlying data’s rate of change shifts significantly.
What It Is Not
- Fine-tuning and RAG are not mutually exclusive, they're frequently combined in the same system.
- RAG is not 'the model learning.' The base model is unchanged; only the context available to it at query time changes.
- Fine-tuning is not a fix for stale or missing data. It addresses behavior and style, not real-time facts.
Comparison
| Attribute | Fine-Tuning | RAG |
|---|---|---|
| What changes | The model’s weights | The context supplied at query time |
| Best for | Consistent style, format, specialized tasks | Frequently changing or large factual data |
| Update cost | Requires retraining | Update the retrieval source directly |
| Traceability | Harder to trace a specific answer’s origin | Can cite the retrieved source directly |
See also
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