AI Prompting For SMBs & Entrepreneurs: How to Easily Become a Master at ChatGPT Using the STAR Method
- W. Daniel J. Parfitt
- Oct 26
- 3 min read
Updated: Oct 28
Summary:
You don’t need to be a coder to get great AI results—you need to be a great communicator.
The STAR method, long used in business communication, is a natural fit for AI prompting.
We’ll break down STAR, map it to Dan Martell’s 4-step process, and show real-world SMB examples.
You’ll leave with a reusable framework for clearer, faster, and more accurate AI outputs.
Introduction: AI Is Listening—But Are You Speaking Its Language?
If you’ve ever left an AI chat feeling frustrated by vague, irrelevant, or bloated results, you’re not alone. Many people assume the problem is technical—that they need to learn coding, prompt engineering, or some “AI magic.” Others make the mistake of inputting a few simple words and assume the first draft is ready for publishing. But Sean Grove of OpenAI recently reminded us of something game-changing:
As AI advances, you don’t need to be a technical expert—you just need to be an expert communicator.
That’s right. If you can structure your thoughts clearly in a meeting, interview, or proposal, you can structure them for every AI LLM (Large Language Model) out there. And there’s already a tried-and-true communication framework that works perfectly for this: the STAR method.
What Is STAR and Why It Works for AI
Originally popularized for job interviews and performance reviews, STAR stands for:
Situation — Set the scene with context and background.
Task — Clearly define what needs to be done.
Action — Explain the steps, approach, or constraints.
Result — Define what success looks like and the desired format.
This structure isn’t just good for people—it’s good for AI because it removes ambiguity. When you give an AI the right context, the exact objective, the steps it should consider, and what the output should look like, you massively increase your chances of getting a spot-on result on the first try.
Dan Martell’s Process vs. STAR: Same Principles, New Language
Dan Martell has shared a 4-step prompting process as follows: Role, Context, Command, Format. Sound familiar?
Martell's process uses the very same communication principles as STAR. It's valuable and effective. Here’s how they map directly to each other:
DAN MARTELL | STAR | DESCRIPTION |
|---|---|---|
Role | Situation | Define the AI's role or perspective and give background. |
Context | Situation | Set the scene for the task. |
Command | Task/Action | Specify the objective and constraints. |
Format | Result | Define output structure and style. |
However, not everyone wants to learn a new acronym for every advancement in technology. That's why I recommend leveraging the already familiar STAR method for AI prompting. By reframing Martell’s process in STAR terms, we make it instantly familiar to anyone in business—especially SMB owners and leaders who’ve been using STAR in communication for years.
Using STAR for AI Prompting: The Steps
Situation: Tell AI who it is, who it’s speaking to, and the business context. Example: “You are a marketing strategist helping a boutique coffee roaster expand their wholesale accounts.”
Task: Give one clear objective. Example: “Write a LinkedIn post to announce our new wholesale partnership program.”
Action: Add specifics—tone, word count, must-haves, and no-gos. Example: “Tone: friendly yet professional. Length: 120–140 words. Mention sustainability practices.”
Result: Define exactly what the output should look like. Example: “Provide the post in a single paragraph followed by 3 suggested hashtags.”
Why STAR Beats “Just Asking”
Without structure, prompts tend to be vague, and vague prompts lead to generic, inaccurate, or overly verbose responses. STAR turns your prompt into a blueprint, making AI’s “thinking” more efficient and aligned with your intent.
The more you use STAR, the more natural it becomes. Before you know it, you’ll be writing AI prompts the same way you’d write a brief for your team.
Real-World SMB Use Cases
Let's put this into practice and see what kind of results you can generate for yourself!
Example: Internal Process Summary
Situation: You are an operations consultant advising a small logistics company.
Task: Summarize the new delivery SOP for drivers.
Action: Highlight 3 key changes; keep the language simple for non-technical staff.
Result: Produce a bullet-point list no longer than 150 words.
Note that for an example like this, you're going to first need an AI or custom GPT fully trained on your business operations. An untrained AI isn't equipped to create or summarize internal processes like this. Consider hiring an AI expert or outside consultant like Leading Brand.
Go Be an AI Master
The next time you open ChatGPT, Claude, or Gemini, try framing your request with STAR. You’ll get clearer, faster, more usable results—and you’ll never again have to say, “That’s not what I meant.”
Comment below what kind of results you have using this method!
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