RAG vs Fine-Tuning: how to choose the right AI strategy

When companies integrate AI into their workflows, they face a critical architectural decision: Should we retrieve dynamic files using RAG, or train a custom model via fine-tuning? The answer depends entirely on your data latency and update frequency.

RAG is for dynamic, factual data

If your model needs access to real-time stocks, private client databases, or constantly changing metrics, RAG is the default choice. By fetching updated data blocks from vector databases at runtime, RAG grounds answers in text you control and doesn't require constant retraining. It also gives you something fine-tuning cannot: a citation trail back to the source document.

Fine-tuning is for tone, formatting, and logic

If you need a model to follow strict industry syntax, output specialized code styles, or execute reasoning paths unique to your company, fine-tuning is required. Fine-tuning teaches the model *how* to speak, while RAG teaches it *what* to say.

Use RAG for knowledge. Use Fine-Tuning for style and formatting.