Matt Zimmerman – AI Prompt CREATORS

AI Prompt CREATORS by Matt Zimmerman explores how structured instructions can guide AI applications through creative, technical and business tasks. The course covers prompt design, model behavior and debugging, with examples ranging from marketing content to code and data workflows.

Zimmerman draws on more than 2,600 hours of prompt engineering and software development, including his work building the AI writing tool ZimmWriter.

Prompt engineering skills covered

• 1. Understanding AI prompt engineering.
• 2. Techniques for boosting creativity.
• 3. Tools for workflow automation.
• 4. Best practices for effective prompts.
• 5. Strategies for engaging AI applications.
• 6. Methods for evaluating prompt success.

Why prompt structure matters

Conversational instructions can leave room for ambiguity or lead an AI model away from the intended task. Zimmerman examines ways to identify misunderstandings and hallucinations, refine multi-step instructions and avoid words that can interrupt a model’s response.

The course also draws on linguistic and psychological concepts, including ideas from classical rhetoric, contract law and code, to explore how instructions influence model behavior. Examples include prompts for blogs, marketing copy and code snippets.

Prompting principles and techniques

The course introduces a Sun Tzu-inspired principle for deciding when free-form chat is less useful than a structured prompt. It also examines a common instruction-writing mistake and the Rule of One, which separates a prompt into individual, repeatable actions.

Advanced techniques include the Don’t Say “It” Rule for reducing pronoun ambiguity, few-shot prompting with a small set of examples, and delimiters for separating instructions. Jenga-style stacking is used to assemble more complex, layered prompts.

Model behavior and prompt debugging

Lessons consider differences in architecture and bias among AI models, including how those differences affect their responses to instructions. Partial outputs can be examined during a prompt to help identify where a response is going off track.

A five-second debugging exercise checks whether a model is following rules and helps pinpoint problems in the prompt.

Psychological and linguistic methods

Zimmerman discusses Pavlovian conditioning, contract-law concepts and repetition as ways to explore model compliance and pattern bias. The Absurd Hypothesis method is presented as a way to reveal contradictions in responses.

Applications for content, code and workflows

Examples cover SEO-oriented blog posts, sales emails and social media content, as well as code snippets, API calls and data transformations. The course also explores prompts for multi-step research summaries and dynamic data queries.

Course curriculum

The curriculum moves from defining a goal and understanding model behavior to formatting, testing and refining more complex prompts. Lessons use video tutorials, examples and prompt labs to put the principles into practice.

• Know Your Goal: Set a clear objective and recognize the make-believe fallacy.
• AI Models: Compare instruction-following behavior across GPT-3.5, GPT-4 and open-source architectures.
• Chat Bias: Identify conversational defaults that can interfere with precise responses.
• Cause & Effect Ambiguity: Examine wording that can confuse relationships between causes and effects.
• Subjective vs. Objective Words: Choose language with an eye toward more consistent outputs.
• Too Many Instructions: Balance the amount of detail in a prompt against its length and complexity.
• Prompt Formatting: Organize multi-step commands using clear delimiters.
• Few-Shot Prompting: Use a limited number of examples to guide complex tasks.
• Brainpower: Test how rule complexity affects a model’s ability to follow instructions.
• Pattern Bias: Use repetition to reinforce desired behavior.
• Last But Not Least: Identify trigger words that may cause a model to abandon a task.
• The Rule of One: Divide instructions into single-action units.
• The Don’t Say “It” Rule: Reduce ambiguity caused by pronouns and unclear references.
• Playing Make-Believe: Explore when roleplay can help a prompt and when it can get in the way.
• Flavor: Add humanized voice elements without introducing unwanted bias into the content.

Videos, cheat sheets and case studies

The course provides over six hours of video training, demonstrations and hands-on labs. Prompt principle cheat sheets offer quick references, while ZimmWriter case studies examine real-world prompts used to produce millions of blog words.

Course materials include videos and documents.

Who can use these prompting methods

The course is designed for content strategists and marketers, developers and data scientists, entrepreneurs and founders, writers and editors, and consultants and agencies. Its examples address both creative work and the construction of AI-driven applications.

No prior AI or coding experience is required.

Frequently asked questions

What does AI Prompt CREATORS cover?

The course covers structured prompting, model behavior, ambiguity, examples, formatting and debugging. It also applies these ideas to content, code and multi-step workflows.

Who is AI Prompt CREATORS suited to?

It is intended for marketers, content strategists, developers, data scientists, founders, writers, editors, consultants and agencies working with AI.

Do I need coding or AI experience?

No prior coding or AI experience is required.

What course materials are described?

The course includes video training, demonstrations, examples, prompt labs, cheat sheets and ZimmWriter case studies. The materials include videos and documents.