Fine-tuning adapts a pre-trained model to specialised tasks using a small dataset. OpenAI provides an API for GPT-3.5 Turbo that lets you upload prompt and completion pairs. This process permanently shapes the model, unlike prompt engineering which only supplies context at runtime.
Well curated training data is critical. OpenAI recommends at least fifty examples formatted as chat messages. Each data point should include system, user and assistant roles so the model learns the desired behaviour.
A fine-tuned GPT-3.5 model can approach GPT-4 quality while running faster and cheaper. With accurate prompts and careful validation, companies can replace heavier models and cut inference costs dramatically.
Klu.ai helps collect interactions and feedback so you can filter the best examples before fine-tuning. The platform will soon automate dataset creation from logs and evaluations.