Fine Tuning

Fine tuning adapts a pre-trained model to your specific data and task, improving performance where it matters most. You start with a model that already knows a lot, then teach it more using your own examples.

How it works

1. 1Start with a pre-trained model that has learned general patterns.
2. 2Add your training data — examples specific to your task or domain.
3. 3Train (fine tune) the model — it adjusts its weights to fit your data.
4. 4Get a specialized model that performs better on your specific task.
5. 5Deploy & use the tuned model in your product or workflow.

Key points

• Better accuracy on your specific task or domain.
• Faster results with less prompting and fewer examples.
• Lower costs by using smaller models effectively.
• Adapts tone, style and behaviour to your needs.

Why it matters

Fine-tuning is the tool when you need consistent style or a narrow, repeated task. It bakes behaviour into the model, unlike RAG which injects facts at query time.

Frequently asked questions

Fine-tuning or RAG?

Fine-tune for consistent style or a narrow task; use RAG for changing or private facts. They solve different problems.

Do I need lots of data?

You need enough good, task-specific examples; techniques like LoRA make efficient fine-tuning possible with less.