Since the release of GPT‑3.5 Turbo, developers and businesses have asked for the ability to customize the model to create unique and differentiated experiences for their users. With this launch, developers can now run supervised fine-tuning to make this model perform better for their use cases.

In our private beta, fine-tuning customers have been able to meaningfully improve model performance across common use cases, such as:

In addition to increased performance, fine-tuning also enables businesses to shorten their prompts while ensuring similar performance. Fine-tuning with GPT‑3.5‑Turbo can also handle 4k tokens—double our previous fine-tuned models. Early testers have reduced prompt size by up to 90% by fine-tuning instructions into the model itself, speeding up each API call and cutting costs.

Fine-tuning is most powerful when combined with other techniques⁠(opens in a new window) such as prompt engineering, information retrieval, and function calling. Check out our fine-tuning guide⁠(opens in a new window) to learn more. Support for fine-tuning with function calling and gpt-3.5-turbo-16k will be coming later this fall.