[SystemSafety] Engineering with the mother of all prompts

[Les Chambers]

Hi All 
In working with ChatGPT, it turns out that not all prompts are equal. Some 
return more accurate/useful information than others. To get help with the 
emerging discipline of prompt engineering the best mentor is ChatGPT itself. 
Enter the “Mother Prompt”.

Courtesy of the Exponential View by Azeem Azhar, I have found the following 
extremely useful: 

You may ask ChatGPT to help you design a prompt, according to your specific 
needs. Ethan Mollick has usefully designed this “mother” prompt: 
	•	GPT-4 Prompt: Help me craft a really good prompt for ChatGPT. 
First, ask me what I want to do. Pause and wait for my answer. Ask questions to 
clarify as needed. Second, once you have the information suggest a prompt that 
include context, examples, and chain of thought prompting where the prompt goes 
step by step through the problem. Third, show what your response as ChatGPT 
would be to the prompt. Fourth, ask if the user has any suggestions and help 
them revise the prompt.

Prompt efficiency can also be improved by using tried-and-true prompt 
templates. For example:

Is there a relationship between AAAA and BBBBBB? If so, give me 7 examples.

Apply the concept of Y to X. Give me 7 options. Let’s take this step by step to 
make sure we have the correct answer.

Let’s analyse X by Y. Give me multiple scenarios, each with a quantitative 
assessment of Z and the assumptions underlying the prediction.
{Example: 
Let's analyse the future global adoption of electric vehicles by 2050. Give me 
multiple scenarios, each with a precise percentage of adoption and the 
assumptions underlying the prediction.
}

What would need to be true for [unlikely event] to happen? Give me a list of 
tangible elements, and how to measure them. 
{Example: 
What would need to be true for AI not to enable economic growth? Give me a list 
of tangible elements, and how to measure them.
}

Background
While large language models like GPT-4 generate text based on probabilities 
learned from training data, the output isn’t deterministic for a given prompt. 
Instead, it includes a level of randomness, often controlled by a parameter 
known as “temperature”.

When you provide the same prompt to a language model multiple times, it’s like 
rolling a weighted die each time. The weights (probabilities) haven’t changed, 
but because there’s an element of randomness, you can still get different 
results.

This means that ‘how’ you prompt matters, as the model may choose different 
options depending on the wording and its associations. It also means that the 
output will always include some element of randomness. The more outputs you 
get, the more ground they will cover. This is an important principle that I 
use: ask for multiple options, so that I can decide what is most useful for me, 
and I can see a range of possibilities. 

Another reason to ask for multiple answers is the evidence around chain of 
thought (CoT) prompting. CoT is a technique that has been shown to drastically 
improve output by encouraging a chatbot to explain its reasoning. Further 
testing has found that saying “Let’s work this out in a step by step way to be 
sure we have the right answer” yields the best zero-shot prompt results. I 
incorporate this into my prompts, or ask for multiple results, depending on my 
needs. 

Happy prompting.

Cheers
Les


--

Les Chambers

les at chambers.com.au

+61 (0)412 648 992