But I can make a similar argument about a simple multiplication:

- You have to know how the inputs are processed.

- You have to left-shift one of the operands by 0, 1, ... N-1 times.

- Add those together, depending on the bits in the other operand.

- Use an addition tree to make the whole process faster.

Does not mean that knowing the above process gives you a good insight in the concept of A*B and all the related math and certainly will not make you better at calculus.

I'm still confused by what you meant by "actual reasoning", which you didn't answer.

I also fail to understand how building what you described would not help your understanding of multiplication, I think it would mean you understand multiplication much better than most people. I would also say that if you want to be a "multiplication engineer" then, yes you should absolutely know how to do what you've described there.

I also suspect you might have lost the main point. The original comment I was replying to stated:

> Don't really see why you'd need to understand how the transformer works to do LLMs at work.

I'm not saying implementing speculative decoding is enough to "fully understand LLMs". I'm saying if you can't at least implement that, you don't understand enough about LLMs to really get the most out of them. No amount of twiddling around with prompts is going to give you adequate insight into how an LLMs works to be able to build good AI tools/solutions.