Fine-Tuning Shifts Attention, Not Knowledge

Fine-tuning adjusts what patterns a model prioritizes. It can make the model more likely to respond in a particular style, format, or domain vocabulary.

What fine-tuning doesn’t do: add new reasoning capabilities or factual knowledge that wasn’t implicit in the base model.

What fine-tuning is good for:

• Adapting output style and tone
• Shifting toward domain-specific vocabulary
• Adjusting response format preferences
• Emphasizing certain types of patterns over others

What fine-tuning can’t do:

• Add facts the model never saw
• Create reasoning capabilities that don’t exist in the base model
• Fix fundamental limitations of the architecture

The misconception: “We’ll fine-tune on our proprietary data and the model will know our business.” Reality: the model will talk like your business, but its actual knowledge comes from the base model’s training.

For adding domain knowledge, RAG (retrieval at query time) is often more effective than fine-tuning (baking into weights).

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