Depending on your definition of "learn", you can also use something akin to ChatGPT's Memory feature. When you teach it something, just have it take notes on how to do that thing and include its notes in the system prompt for next time. Much cheaper than fine-tuning. But still obviously far less efficient and effective than human learning.
Re:Google Antigravity
by OtherShrezzing on Tuesday November 18, 2025 @02:44PM
(#45971049)
I think it’s reasonable to say that different approaches to learning is some kind of spectrum, but that contemporary fine tuning isn’t on that spectrum at all.
> With an LLM, you have to specifically setup a convoluted (and potentially financially and electrical power expensive) system in order to provide MANY MORE examples of how to improve via fine tuning or other training actions.
The only way that an AI model can "learn" is during model creation, which is then fixed. Any "instructions" or other data or "correcting" you give the model is just part of the context window.
Fine tuning is additional training on specific things for an existing model. It happens after a model already exists in order to better suit the model to specific situations or types of interactions. It is not dealing with context during inference but actually modifying the weights within the model.
Re:Google Antigravity
by MangoToupe on Wednesday November 19, 2025 @09:22AM
(#45979831)
Retraining (or fine tuning) isn't the same thing at all and I think that's obvious
With a human, you give them feedback or advice and generally by the 2nd or 3rd time the same kind of thing happens they can figure it out and improve. With an LLM, you have to specifically setup a convoluted (and potentially financially and electrical power expensive) system in order to provide MANY MORE examples of how to improve via fine tuning or other training actions.