Hello everyone !
Hopefully my answer will be helpful.
If I explain simply:
- Fine-tuning is giving new knowledge to an AI by retraining it. You « feed it » new data that will now be part of the AI, therefore changing the core of that specific AI. Just like if you read book - it’s somewhere in your brain.
- Custom GPTS instead are all based on the same AI model that remains unaltered. You « just » give the GPT instructions and documents, they can be modified at anytime to update the GPT to your convenience. Like a human wearing a pair of glasses, it changes the way it sees the world, but not the person itself.
The one misleading difference between fine-tuning and GPTs or other agents/bots/AI (they can be called in a multitude of ways) is that in both case you give it new data. It is really in the way the data is used by the AI that makes the big difference. In the first case the AI is modified in its core, while in the second case it is really about providing instructions to guide the existing AI (without modifying the core).
Why do you want to use one or the other ? Context & cost. Both methods aim to specialize an AI but the outcomes are different. Fine-tuning is more complex and expensive, you need new quality data that will lead to consolidation of knowledge for a tiny part the AI systems - you literally improve the system in a very incremental way (if done correctly). While the custom GPTs approach is much less expensive and more accessible (no-code/low-code - everybody can do it). You do not improve the system but you activate the proper part of the AI brain to get the best out of it.
Personal thoughts & considerations - customs GPT to me appear as a knowledgeable librarian. I can give it specific documents if I want to and I can even instruct it to only respond using these documents. That can be amazing to create specialized assistant using a specific book, framework, knowledge base - whatever you think of that the AI can understand. That is even better when you consider how easy it is to update them, just change the instructions or the documents. Eventually, when you have improved enough a GPT and that the nature of your use-case is relevant (not an info changing frequently - fine-tuning is not an update), it might be considered for fine-tuning.