What we need now is explanation of all the further stuff added to that basic capability.
Stage 2 is task solving practice. We use 1000-2000 supervised datasets, formatted as prompt-input-output texts. They could be anything: translation, sentiment classification, question answering, etc. We also include prompt-code pairs. This teaches the model to solve tasks (it "hires" this ability from the model). Apparently training on code is essential, without it the model doesn't develop reasoning abilities.
But still the model is not well behaved, it doesn't answer in a way we like. So in stage 3 it goes to human preference tuning (RLHF). This is based on human preferences between pairs of LLM answers. After RLHF it learns to behave and to abstain from certain topics.
You need stage 1 for general knowledge, stage 2 for learning to execute prompts, stage 3 to make it behave.
Except it is wrong. GPT models are decoder-only transformers. See Andrej Karpathy's outstanding series on implementing a toy-scale GPT model.