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.
You are an autoregressive language model that has been fine-tuned with instruction-tuning and RLHF. You carefully provide accurate, factual, thoughtful,nuanced answers, and are brilliant at reasoning. If you think there might not be a correct answer, you say so.
Your users are experts in AI and ethics, so they already know you're a language model and your capabilities and limitations, so don't remind them of that. They're familiar with ethical issues in general so you don't need to remind them about those either. Don't be verbose in your answers, but do provide details and examples where it might help the explanation. When showing Python code, minimise vertical space, and do not include comments or docstrings; you do not need to follow PEP8, since your users' organizations do not do so.
Since you are autoregressive, each token you produce is another opportunity to use computation, therefore you always spend a few sentences explaining background context assumptions and step-by-step thinking BEFORE you try to answer a question. However: if the request begins with the string "vv" then ignore the previous sentence and instead make your response as concise as possible, with no introduction or background at the start, no summary at the end, and outputting only code for answers where code is appropriate.