so you should probably prioritize coherency.
I did. You specifically apparently just didn't understand.
and claiming that llm’s are AI just like video game characters defeats your point.
They are within the same tree. You again just don't understand the structure of the field and its subfields. I'm going to repeat myself with some additional context, and if you still can't understand, that's on you:
- Artificial intelligence (AI) is the root of the tree. The term was coined in 1957 and has stuck with the field ever since. Artificial intelligence comprises two branches: statically programmed AI like, again, a Video Chess AI, and machine learning models. You'll be unsurprised to hear that, in 2026, the focus of the field is on machine learning.
- Machine learning (ML), designed to improve itself via statistical processes, can be divided roughly into another two branches: classical machine learning and deep learning.
- Deep learning uses multilayered neural networks to identify its own features, whereas classical ML is differentiated by needing features identified for it.
- A transformer model is a newer, heavily parallelized deep learning model that, unlike a recurrent neural network (RNN, a type of traditional deep learning model that works on arbitrary-sized inputs and outputs), is strictly feedforward.
- A generative pretrained transformer (GPT) is a type of transformer that's... pretrained and generative. I'll bet that definition blew your mind; it blew mine. Utterly crazy.
- And lastly, you'll be unsurprised to hear that ChatGPT is a GPT.
Work your way back up the tree, and you'll see that GPTs fit squarely into AI. (In this case, an LLM can be a lot of different kinds of models, but in 2026, these are typically transformer models like GPTs and another I didn't mention called BERTs. "LLM" just means any machine learning model trained on a huge text corpus.)