Fine-tuning allows businesses to train a version of GPT on their own proprietary data to improve performance on specific tasks.

When to Fine-Tune

• You need consistent formatting the prompt alone can't achieve
• You have thousands of high-quality examples
• Latency and cost savings matter at scale

When NOT to Fine-Tune

• You haven't tried prompt engineering first
• Your dataset is small or low quality
• You need real-time knowledge updates

Reference:

Wikipedia: Fine-Tuning

https://en.wikipedia.org/wiki/Fine-tuning_(deep_learning)