What this actually involves
Most teams reach for fine-tuning too early. We start by proving that retrieval and prompting cannot close the gap, then adapt a model only where the task is narrow, the data exists, and the economics work. Often the honest answer is that you do not need this.
- A build-versus-prompt assessment before any training runs
- Dataset construction, cleaning, and held-out evaluation splits
- LoRA and QLoRA adaptation with reproducible training configs
- Serving setup with a documented rollback to the base model
When it fits
- Prompting and retrieval have been tried and measured, and the gap is in style, format, or one narrow skill.
- You have hundreds or thousands of clean, correctly labeled examples.
- A smaller tuned model could match a larger one at a lower cost per request.
When it does not
- The gap is missing knowledge. Retrieval fixes that more cheaply than training does.
- You have few examples, or the labels disagree with each other.
- You cannot afford to retrain each time the base model changes.
What we need from you
- The examples, plus a rule for what counts as correct.
- A test set that stays out of training.
- Your current baseline result, so we can show whether training helped.
Ready to move from AI experiments to AI in production?
A thirty-minute call. We assess the use case, name the risks, and tell you honestly whether we are the right fit.