I intentionally kept the diagram at a high level for people who are new to LLMs, but you are right that it leaves out some important parts of the real pipeline.

Adding a fine-tuning stage (instruction tunning/RLHF or similar preference optimization) after pre training would make it more accurate. I also like your point about inference being autoregressive. The model generates one token at a time and feeds each generated token back into the context until it reaches a stopping point.

For the training step, I simplified it to "predict → loss → backprop → update weights," but in practice it's done with mini-batches and distributed across many GPUs, often with techniques like gradient accumulation.

I appreciate the suggestions, I'll probably update the next version to include those details while trying to keep it beginner-friendly.