Prompt Engineering for Business Owners

Reader payoff: 4 concepts to turn AI into a revenue-driving machine in days.

Last week, I presented to a group of seasoned business owners. Smart folks. Sharp operators.

As we went around the room, I asked each of them how they were currently using AI.

The answers were familiar:

"We're experimenting with tools."

"Trying a few things in ChatGPT, Perplexity, Grok."

"Some cool outputs... but also a lot of misses."

Then one founder said what everyone was thinking:

"Honestly, it feels sometimes like pulling lottery tickets. Sometimes I win. A lot of times, I get junk that I don’t end up using..."

Others jumped in. They were frustrated. Not just by their own results, but by the "AI slop" flooding their inboxes. Vague content. Off-brand emails. Copy-paste fluff that screams "bot."

They didn't want to create that. And they didn't want to consume it either.

In this issue, you’re going to see exactly why this might be occurring to you (hint: it’s not AI’s fault) and more importantly…how to fix it for good.

Because AI that’s used wrong doesn’t just waste time…it makes your brand look sloppy. It fills your pipeline with junk. And a brand with lots of “noise” will get pushed to the side until it’s forgotten completely.

Today isn’t about becoming an AI expert. It’s about training it to do the job right…just like you would with a smart team member.

Here we go.. And, like last week…this is an email you’re going to want to bookmark and come back to again and again.

How to Use AI to Get Solid Results

If you’ve ever observed a great leader, you already know this truth:

Great communication = great results.

The best business owners are clear about where they’re going. They set a sharp vision, define what success looks like, and make sure their team has the tools and context to get there.

Those types of leaders? Their teams turn traction into results. With focus. With speed.

But we’ve all seen the flip side.

The leader who keeps everything in their head. Who hopes the team just “gets it.” Who drops vague tasks with no direction and then wonders why nothing is happening.

That kind of leadership creates guesswork, which kills any form of progress and causes staff to check out.

Most people are using AI like that second leader.

They toss a prompt into ChatGPT like, “make this better,” and hope for the best.

But AI is on your team now. And like any team member, it needs to be trained.

To get results, you have to treat AI like a really smart new hire. That means giving it the why, the what, the how, and the guardrails.

Let’s break down five ways to turn AI into truly one of your best staff members.

Concept 1: What Actually Is AI?

Let’s clear something up: AI isn’t magic.

At the heart of every AI tool you use—from ChatGPT to that auto-summarizer in your CRM—is a Large Language Model (LLM). It’s not thinking like a human. It’s not “understanding” you in the way your team would.

It’s doing something simpler—and more powerful.

An LLM is a pattern machine. It’s trained on mountains of text (books, articles, chats, code) and it’s learned to guess the next word in a sentence based on the words before it. That’s it. One token at a time, lightning fast.

But once you really get that? You stop treating AI like a mind reader… and start treating it like a machine you can steer.

Here’s why this matters:

Everything is moving toward AI-powered interfaces. CRMs, dashboards, design tools. They’re all becoming prompt-based.

Your ability to describe clearly what you want is the new literacy. The better your instructions, the better your outcomes.

But most people? They prompt before they plan.

They don’t define the outcome. They don’t know what success looks like. Then they blame the model.

The real upgrade isn’t in the tool. It’s in how you think before you type.

Master that…and you won’t just use AI. You’ll command it.

Concept 2: Thinking Changes Everything

If you want better AI output, it starts with how you think before you prompt.

Too many founders and operators treat prompting like a task exercise. But the real game changer is learning to treat it like a thinking exercise so you setup the AI to win.

Before you write a single word, ask yourself these three questions:

1. What Specific Outcome Do I Want?

“Write me an email” is not an outcome. It says nothing about the result we actually want.

Better:

“Write a follow-up email to a potential client who showed interest in our SEO services three weeks ago, responded positively to our proposal, but hasn’t replied to our last two check-ins.”

The better version gives:

🔹 Relationship context…

🔹 Timeline…

🔹 Current status…

🔹 Intent of the message…

Outcome clarity should not be optional…it’s the difference between a specific result and AI slop.

2. What Does the AI Need to Know to Succeed?

AI doesn’t know your business, by default. It doesn’t know your tone, your goals, or your client quirks.

(Unless of course you are using our Company OS…then it has all of this context built in for you)

You have to feed AI what it needs:

🔹 Your company voice and values

🔹 Relevant history (client, project, interactions)

🔹 What success looks like in this scenario

🔹 Constraints (word count, tone, exclusions)

Think of it like briefing a new team member. The more you assume, the more they guess and the more they miss.

3. How Will AI Know What’s “Good”?

If you don’t know what “good” looks like, how will AI?

“Make it professional” is not a standard the AI understands... Try being specific like this instead:

• “Friendly but persistent tone”

• “Reference proposal details”

• “Includes clear next step”

• “Feels human and not templated”

This isn’t nitpicking…it’s leadership.

Clear instructions = successful outcomes.

Let’s See It In Action…

Bad prompt:

“Write a professional email to a client about our project delays.”

Too vague. We don’t know:

• What project?

• What kind of delay?

• Who the client is?

• What they care about?

• What “professional” even means here?

Now try this:

“Write an email to Emmy, our long-term client who’s been working with us for two years on quarterly marketing campaigns. Let her know our current campaign will be delayed one week due to a landing page integration issue. She values direct, detailed updates. Keep the tone apologetic but confident. Include the new launch date, explain the issue, and offer a 10% discount for the inconvenience. Keep it under 150 words.”

Same use case. Totally different result.

Because this time —> the AI actually has input to work with and craft the result you wanted in the first place.

The Breakthrough Moment

This is the shift that separates the dabblers from modern operators:

• ❌ Dabbler: 30 seconds to prompt, 30 minutes fixing a bad output

• ✅ Modern Operator: 5 minutes thinking, 1 minute prompting, result they can use now

That’s when you know it’s clicking…you’re investing thought up front instead of reacting to junk on the back end.

And once this mindset locks in, you can start building entire workflows, systems, and automations on top of it. But first, master this habit:

Think before you type.

Now that your thinking is solid, there’s one more trap to avoid:

Dumping everything into a blob of text and hoping the model understands it the same way you do.

Now let’s talk about a better way to structure your input so the AI responds like a pro.

Concept 3: Your AI Doesn’t Read Like a Human

Key #1 — Here’s a little-known truth: AI reads differently than you do.

You can scan an email, skip a paragraph, or fill in gaps using experience. AI can’t. It processes prompts line by line, word by word…like reading a book where it never gets to flip back a page.

Whatever you feed it first frames the entire output. If that early context is vague, confusing, or missing? The AI builds on a weak foundation.

Imagine you’re training a new team member. You wouldn’t start by saying, “Send a message to Jamie about the project,” without first explaining:

• What project?

• What happened?

• What tone should we use?

• What do we want Jamie to do?

Most people feed prompts to AI with that same lack of structure. Then they’re surprised when the output is off?!

Key #2 — Unlike a staff member such as your Director of Marketing or Fulfillment Coordinator, AI can be ANYONE you want it to be.

But, most business owners treat AI like it should already know who it is supposed to be at the start of any interaction.

They forget to first set the stage: “Here’s who you are while doing it.”

And that’s a big mistake.

The fix?

Specify the role you want it to be before the task.

Here’s a 3-Layer Architecture That Works

To get consistent, usable results, organize your prompt like this:

🔹 Layer 1: Role Context — Who should the AI become?

This tells the model what lens to view the task through. It’s like assigning a costume and script before the performance begins.

Then add in the CORE essentials:

• What’s the situation?

• What must be known up front that affects everything else?

Example:

“You are a B2B SaaS marketing strategist who specializes in driving feature adoption through lifecycle email campaigns.”

🔹 Layer 2: Task Context — What’s the situation?

This gives the specific background it needs to interpret the problem.

Then provide background and context:

• Relevant history

• Additional context that helps support (not do or die)

• Anything helpful, but not essential

Example:

“Our project management tool just launched automated deadline reminders. Customers who haven’t activated it yet are missing time-saving benefits.”

🔹 Layer 3: Instructions — What do you want the AI to do?

Now that it knows who it is and what the situation is, it’s ready to act.

Finally, give the specific task you want done:

• How to format, specific tone or CTA

• How the output should look…word count, etc

• Details that provide the do’s and don’ts

Example:

“Write a friendly, concise email to encourage feature activation. Highlight time-saving, reduce perceived effort, and include a clear CTA.”

First, the AI figures out its game plan in Layer 1, fine-tunes what it knows in Layer 2, and puts it all into action with precision in Layer 3.

Prompt with poor structure:

“Write a summary of our Q3 financials in a clear, confident voice. Make sure to include comparison of results from last year. Our company sells B2B analytics software. The report should highlight YoY growth and mention our Series B raise.”

That order forces the AI to course-correct as new info trickles in.

Prompt with a much better structure:

“You are a CFO preparing a Q3 summary for internal use. Our company provides analytics software to mid-market B2B firms. We raised a $20M Series B this quarter and grew YoY revenue by 38%.

Now the AI knows who it is, what’s important, and how to present it…before it starts writing.

The Context Test

Ask this before you prompt:

👉 If the AI only knew this one thing, would it change how it does the task?

• If yes → That’s critical context → Goes first

• If no → That’s supporting detail → Goes middle

• If it’s about format → That’s instruction → Goes last

This one habit will radically improve how AI performs for you.

When you organize your inputs clearly, the AI doesn’t just spit out words…it understands the job.

The output feels thoughtful, aligned, and tailored. You stop fixing drafts and start shipping results.

Concept 4: Be Specific and Precise

Sometimes (and especially for longer prompts), you can draft a great prompt…hit enter…and still have AI deliver back something that starts strong but goes sideways halfway through.

It’s not you…it may be it’s memory.

AI runs on what’s called a context window—think of it like the model’s short-term memory. Every word you type (and every word it replies with) fills that memory. When it runs out of space, older details start falling off the back end.

So that brilliant setup paragraph you wrote at the beginning? By the time the model reaches your final instruction, it might be gone.

The technical way to think about it is that AI doesn’t count words…it counts tokens. A token might be a full word, part of one, or even punctuation. Models have token limits (think: 8,000 to over 1 million tokens, depending on the version).

Here’s a quick analogy: imagine giving a new employee a one-hour briefing but expecting them to remember every detail from the first 5 minutes by the end of the meeting—without notes. That’s what’s happening inside the model.

The solution?

Instead of writing longer, write tighter.

🔹 Cut fluff (“please,” “kindly,” “just”, “I’d like to think about doing this in a way that kinda moves like this but without that”)

🔹 Combine redundant phrasing

🔹 Prioritize clarity over courtesy

Shorter prompts aren’t just cheaper…they’re smarter. When you trim the fat, you also sharpen your thinking.

Efficient prompts force precision. They help the AI stay focused on what you actually want.

Less noise. More signal. Better results.

Final Thoughts

If you skimmed straight to the end, here’s the big idea:

AI isn’t your magic wand…it’s just like your newest team member. And just like your best staff, it needs structure, clarity, and leadership to succeed.

We broke down 4 simple concepts that help you move from vague prompting to precision results:

• AI responds best when you tell it who to be, what it’s solving, and how to solve it.

• Clear thinking before typing saves hours of frustrating iteration.

• Structured inputs lead to smarter outputs.

• The order you give instructions matters more than you think.

• Short, efficient prompts aren’t just cheaper—they’re sharper.

Great prompts aren’t about tricks. They’re about training.

When you prompt like a leader, AI starts delivering like a pro.

This is Issue 27 of Modern Operators. We help founder-led businesses scale smarter by turning clarity into their ultimate growth lever