In my article, I dive into the growing shift away from traditional fine-tuning in AI, especially with the advent of models like GPT-5.
Fine-tuning has long been the go-to method for adapting models to specific tasks, but with more powerful pre-trained models like GPT-5, we’re seeing a move towards few-shot and zero-shot learning.
These models are able to handle a wide range of tasks with minimal additional training, challenging the need for task-specific fine-tuning.
The real question I’m exploring is whether we’ll continue to rely on fine-tuning for specialized applications, or if this shift towards generalization will dominate, making it more efficient and scalable to work with models that can adapt to new tasks with little adjustment.
For those working with GPT-5 or other similar models, are you finding the need for fine-tuning decreasing in your own projects? Or are there cases where you still see fine-tuning as a critical part of the process?
In my article, I dive into the growing shift away from traditional fine-tuning in AI, especially with the advent of models like GPT-5.
Fine-tuning has long been the go-to method for adapting models to specific tasks, but with more powerful pre-trained models like GPT-5, we’re seeing a move towards few-shot and zero-shot learning.
These models are able to handle a wide range of tasks with minimal additional training, challenging the need for task-specific fine-tuning.
The real question I’m exploring is whether we’ll continue to rely on fine-tuning for specialized applications, or if this shift towards generalization will dominate, making it more efficient and scalable to work with models that can adapt to new tasks with little adjustment.
For those working with GPT-5 or other similar models, are you finding the need for fine-tuning decreasing in your own projects? Or are there cases where you still see fine-tuning as a critical part of the process?