Fine-tuning

A general-purpose AI model is a bit like a highly educated generalist - someone who has read widely across many subjects and can hold a reasonable conversation about almost anything. Fine-tuning is like sending that generalist to do a deep specialisation. They spend months immersed in one field - reading nothing but medical research papers, or legal contracts, or customer support transcripts - and come out much more capable in that specific area.

The way this works technically is that after the initial large-scale training (which costs enormous amounts of money and takes months), you run a second, smaller training process using a focused dataset relevant to your use case. This second training adjusts the model to perform better on your specific type of content. The model does not forget everything it learned before - it layers new expertise on top of existing capabilities.

Fine-tuning is particularly valuable when you need the AI to adopt a specific style, follow particular rules consistently, or handle a type of content that is not well represented in general internet text. A model fine-tuned on your company's customer emails will respond in your brand voice more naturally than a general model ever could, just by prompting it differently.

The cost and complexity of fine-tuning has dropped significantly in recent years. Newer techniques allow you to adjust only a small part of the model's parameters rather than retraining the whole thing, which means companies with modest budgets can now build fine-tuned models that would have required enormous resources just a few years ago.

Fine-tuning and RAG are often used together. RAG gives the model access to specific documents at the moment of answering. Fine-tuning teaches the model to behave differently - to adopt a certain style, use specific terminology correctly, or follow a particular workflow - regardless of what documents it is reading.