OpenAI Fine-Tuning — Custom GPT Model for Your Specific Task

I will fine-tune a GPT model via the OpenAI API — covering dataset preparation in OpenAI's JSONL format, training job configuration, hyperparameter selection, training monitoring, validation loss tracking, and evaluation of the fine-tuned model against your quality baseline. OpenAI fine-tuning is the lowest-friction path to a custom model — the managed training infrastructure eliminates GPU setup and framework configuration — but requires careful dataset preparation and hyperparameter selection to produce a fine-tuned model that genuinely outperforms a well-prompted base model rather than simply learning to mimic training data formatting.

The fine-tuning covers dataset preparation and upload, training job configuration (epochs, batch size, learning rate multiplier), training monitoring with loss curve analysis, fine-tuned model evaluation across your quality dimensions, and a comparison against your prompted baseline.