Creating a Meta Prompt for a specific Topic and a Specific Audience

The goal is to create a "template" or "algorithm" for generating content.

TL;DR The meat is here and in this chapter only: Creating Meta Prompts

Programming ChatGPT without Programming

While we're not writing code in a traditional sense, we're using the logical and structured thinking that's foundational to programming to enhance our content creation process.

We are leveraging the principles of programming by defining a structure (or "function"), setting parameters (or "variables"), and generating outputs — and applying it to content creation.

This approach allows us to systematically and consistently produce tailored content prompts (or outputs) based on specific inputs. It's a more structured way of thinking about content creation.

This way is particularly useful when you want to scale content production or ensure consistency across different pieces of content.

Metaprogramming and the Concept of Meta Prompts

The term "meta" implies a level of abstraction, self-reference, or "about its own category".

Therefore "Meta prompts" seem an apt term for what we want to accomplish:
Our idea, in essence, is "meta content creation," where we are setting up a structure to generate content prompts.

Metaprogramming is when you write programs that generate or manipulate other programs (or themselves). Or, differently put: Metaprogramming is where programs have the ability to treat other programs as their data.

This means that a program can be designed to read, generate, analyze, or transform other programs (or even itself).

But rather than trying to focus on coding concepts or paradigms, I used ChatGPT to understand the nuances of what we are doing here by using the universality of programming concepts and how they are applied to our problem: so basically, as a vehicle to critical and systematic thinking.

I do not know what programming concepts or programming paradigm maps to what we are trying to do here.

Our meta prompts act like templates, where an algorithm's structure is preserved, but certain steps are deferred to later step implementations. It encompasses also ideas of “procedural generation” where content is generated algorithmically and functional programming taking parameters (specific inputs) to produce tailored outputs.

In our content generation context, we use templates and specific details we plug into the template to procedurally generate content prompts.

The idea, in essence, is "meta content creation," where we are setting up a structure to generate content prompts.

So what it actually requires is—and this is where programming and philosophy meet through math—systematic thinking.

systematic thinking to the realm of content creation.

Meta Prompts

In our context, we're talking about prompts to create other prompts or content. So the term seems to capture the essence of the idea.

However, depending on your background you might understand what we are trying to accomplish here through another lens, or depending on your specific context another term might resonate better with you and your colleagues.

Some alternative terms:

1. Template Prompts: Emphasizes the template nature of the approach.
2. Prompt Frameworks: Highlights the structured approach to content generation.
3. Prompt Generators: Indicates an automated or systematic method to create prompts.
4. Structured Prompts: Focuses on the structured and systematic nature of the approach.
5. Content Scaffolds: Using the analogy of scaffolding in construction, this term suggests providing a structure upon which content can be built.

Creating Meta Prompts

To create a meta prompt tailored to you and your audience, follow this pattern:

1. Identify Expertise: Start with a declaration of expertise to set the context. For example: "You are an expert in [YOUR FIELD] with [X YEARS] of experience."

2. Define the Topic: This will be the main subject of your article or content piece. Choose a topic relevant to your audience's interests or needs.

3. Detail the Prompt: Describe the content's structure, key points, and desired outcomes. Make sure to:

   1. Specify the target audience.
   2. State the main objectives or goals of the content.
   3. Highlight key topics or subtopics you want covered.
   4. Ask for actionable tips, examples, or case studies.
   5. Request supporting data or research if applicable.

Here's a template you can use:

You are an expert in [YOUR FIELD] with [X YEARS] of experience in [SPECIFIC ACTIVITY or SKILL].
Topic: [YOUR TOPIC]
Prompt: Provide a comprehensive guide on [MAIN SUBJECT], specifically targeting [YOUR AUDIENCE]. Dive deep into key topics such as [KEY TOPIC 1], [KEY TOPIC 2], and [KEY TOPIC 3]. Share actionable tips on [SPECIFIC STRATEGY or APPROACH], and if possible, include real-world examples or case studies relevant to the target audience. Support your claims with data and research, and offer a structured approach or process for readers to apply the knowledge. By the end, readers should [DESIRED OUTCOME].

Example Meta Prompt

Let's create a sample meta prompt based on the template on the topic of “Building Effective Teams in Startups”.

This is the prompt:

You are an expert in organizational behavior with 15 years of experience in team dynamics and leadership.
Topic: Building Effective Teams in Startups
Prompt: Provide a comprehensive guide on establishing and nurturing high-performing teams, specifically targeting startup founders and HR managers. Dive deep into key topics such as team formation stages, communication best practices, and conflict resolution. Share actionable tips on creating a positive team culture, and if possible, include real-world examples or case studies from successful startups. Support your claims with data on team productivity and research on team dynamics. Offer a structured approach for founders to build, manage, and grow their teams effectively. By the end, readers should be equipped with tools and strategies to foster a collaborative and results-driven team environment.

Using this template, you can generate meta prompts tailored to any topic relevant to you and your audience.

Does this sound like programming and software development?

Terms like “[YOUR FIELD]” and “Topic” make it seem that you work with variables and classes.

Let’s look at it in the context of our template:

[YOUR FIELD] is a placeholder (or variable) that you would replace with your specific area of expertise.

Similarly, [X YEARS], [SPECIFIC ACTIVITY or SKILL], [YOUR TOPIC], and other bracketed text are placeholders meant to be replaced with relevant information tailored to your needs.

The word "Topic" is not so much a "class" in the programming sense, but more of a header or label indicating the subject matter of the content. It serves as a concise title or theme for the prompt that follows.

In essence, the template is structured with labels (like "Topic:") followed by placeholders or variables (like [YOUR TOPIC]) that you fill in based on the content you want to generate.

This approach allows you to systematically craft meta prompts that are customized for your specific needs and audience.

Meta Prompts: A Programming Analogy

So what is the gap between the worlds of content creation and software development?

You’ll see that it makes little difference, but understanding might help you create using other tools that are not LISP or conversational Ais or APIs.

Well, let’s then look at the concept of meta prompts from a computer programming perspective.

Imagine you're creating a function that produces content prompts. The function takes several parameters, and based on those parameters, it returns a tailored content prompt.

Function Declaration:

def generate_content_prompt(expertise: str, years_of_experience: int, specific_activity: str, topic: str, main_subject: str, target_audience: str, key_topics: list, specific_strategy: str, desired_outcome: str) -> str:
    # ... function body ...

Parameters:

• expertise: The field in which you're an expert (e.g., "data science", "web development").
• years_of_experience: The number of years you've been in that field.
• specific_activity: A particular activity or skill you have within that field.
• topic: The overarching theme or title.
• main_subject: What the content is majorly about.
• target_audience: Who the content is for.
• key_topics: A list of important subtopics to be covered.
• specific_strategy: Actionable strategies or techniques to be discussed.
• desired_outcome: What the reader should achieve or feel by the end.

Function Call: You'd then call this function, passing in the appropriate arguments, to get your tailored content prompt.

content_prompt = generate_content_prompt(
    expertise="web development",
    years_of_experience=10,
    specific_activity="frontend design",
    topic="Responsive Web Design",
    main_subject="designing adaptive websites",
    target_audience="junior web developers",
    key_topics=["media queries", "flexible grid layouts", "scalable images"],
    specific_strategy="using CSS and JavaScript frameworks",
    desired_outcome="be able to create fully responsive websites"
)
print(content_prompt)

Output: The function would return a string that is your tailored content prompt, which you can then use as a basis for creating content.

By framing meta prompts in this manner, programmers can better relate to the concept by seeing it as a function that takes specific parameters to produce tailored outputs.

This analogy bridges the gap between the worlds of content creation and software development.

To make this analogy complete and functional, let's implement the body of the generate_content_prompt function.

Here's a possible implementation:

def generate_content_prompt(expertise: str, years_of_experience: int, specific_activity: str, topic: str, main_subject: str, target_audience: str, key_topics: list, specific_strategy: str, desired_outcome: str) -> str:
    key_topics_string = ', '.join(key_topics[:-1]) + ", and " + key_topics[-1]
prompt = f"""
You are an expert in {expertise} with {years_of_experience} years of experience in {specific_activity}.
Topic: {topic}
Prompt: Provide a comprehensive guide on {main_subject}, specifically targeting {target_audience}. Dive deep into key topics such as {key_topics_string}. Share actionable tips on {specific_strategy}. By the end, readers should {desired_outcome}.
"""

return prompt

Using the function call from before:

content_prompt = generate_content_prompt(
    expertise="web development",
    years_of_experience=10,
    specific_activity="frontend design",
    topic="Responsive Web Design",
    main_subject="designing adaptive websites",
    target_audience="junior web developers",
    key_topics=["media queries", "flexible grid layouts", "scalable images"],
    specific_strategy="using CSS and JavaScript frameworks",
    desired_outcome="be able to create fully responsive websites"
)
print(content_prompt)

The output would be:

You are an expert in web development with 10 years of experience in frontend design.
Topic: Responsive Web Design
Prompt: Provide a comprehensive guide on designing adaptive websites, specifically targeting junior web developers. Dive deep into key topics such as media queries, flexible grid layouts, and scalable images. Share actionable tips on using CSS and JavaScript frameworks. By the end, readers should be able to create fully responsive websites.

This function essentially constructs and returns a tailored content prompt based on the parameters provided.