It's useful to consider the next answer a model will give as being driven largely by three factors: its training data, the fine-tuning and human feedback it got during training (RLHF), and the context (all the previous tokens in the conversation).

The three paragraphs roughly do this:

- The first paragrath tells the model that it's good at answering. Basically telling it to roleplay as someone competent. Such prompts seem to increase the quality of the answers. It's the same idea why others say "act as if youre <some specific domain expert>". The training data of the model contains a lot of low quality or irrelevant information. This is "reminding" the model that it was trained by human feedback to prefer drawing from high quality data.

- The second paragraph tries to influence the structure of the output. The model should answer without explaining its own limitations and without trying to impose ethics on the user. Stick to the facts, basically. Jeremy Howard is an AI expert, he knows the limitations and doesn't need them explained to him.

- The third paragrah is a bit more technical. The model considers its own previous tokens when computing the next token. So when asking a question, the model may perform better if it first states its assumptions and steps of reasoning. Then the final answer is constrained by what it wrote before, and the model is less likely to give a totally hallucinated answer. And the model "does computation" when generating each token. So a longer answer gives the model more chances to compute. So a longer answer has more energy put into it, basically. I don't think there's any formal reason why this would lead to better answers rather than just more specialized answers, but anecdotally it seems to improve quality.