What Is Fine-Tuning?
Fine-tuning takes a pre-trained foundation model and continues its training on a smaller, domain-specific dataset — teaching it the patterns, terminology, and behaviors relevant to your particular use case. This is more efficient than training from scratch and produces models that outperform general-purpose AI on specialized tasks.
When Should You Fine-Tune vs. Use RAG?
Fine-tuning changes how the model thinks and writes. RAG changes what information it has access to. Use fine-tuning when you need the model to adopt a specific style, follow domain conventions, or handle specialized formats. Use RAG when you need the model to reference specific, frequently updated information. Many production systems use both: a fine-tuned model enhanced with RAG retrieval.