Fine-tuning
Fine-tuning is the process of further training an existing AI model on a smaller, specific dataset so it performs better on a particular task or domain.
How Fine-tuning works
Instead of training a model from scratch (extremely expensive), fine-tuning takes an already-capable general model and trains it further on a focused dataset — for example, thousands of examples of your company's ideal customer support replies — so it adopts that specific style or expertise.
Why it matters for your business
Fine-tuning is usually a later step, after you've validated a use case with simpler tools like prompt engineering and retrieval-augmented generation. It's worth considering when you need consistent tone, a specialized skill, or when those lighter-weight approaches aren't precise enough.
Frequently Asked Questions
Should my business fine-tune a model or use RAG?
Most businesses should start with RAG — it's cheaper, faster to update, and doesn't require retraining. Fine-tuning makes sense when you need the model to consistently follow a specific tone, format, or specialized skill, not just recall facts.
Is fine-tuning expensive?
It's more expensive and technical than RAG or prompt engineering, requiring quality training data and compute resources — it's usually a later-stage investment, not a starting point.