Is RAG better than fine-tuning?

RAG excels when you need up-to-date, private, or dynamic data with traceability. It is also better for handling sensitive data, as RAG enhances security and privacy for enterprise use by keeping proprietary data in a secure database under the organization's control, rather than embedding it into the model itself. Fine-tuning is better for consistent output style and domain expertise embedded in the model.

When is fine-tuning worth it?

Fine-tuning is valuable when you have sufficient labeled domain-specific data and require precise control over model behavior and output format.

Can prompting replace fine-tuning?

Prompting enables rapid prototyping and works well for many use cases but cannot fully replace fine-tuning’s ability to embed domain knowledge and ensure consistent outputs.

How do I reduce hallucinations?

Using RAG to ground responses in retrieved relevant data, combined with evaluation and monitoring, greatly reduces hallucinations.

What is the fastest path to production?

Prompting is the quickest to implement, followed by RAG integration. Fine-tuning requires more time for data preparation and training but yields specialized models.