Turn ChatGPT into a Business Engine

Build a CEO Prompt

January 25, 2026

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I used to think “better prompts” meant clever wording. Then I watched a founder spend an hour fighting an AI response that never got closer to what he actually needed. Same model, same question, same frustration. A few days later, I saw the opposite: clean answers, sharp strategy drafts, even hiring suggestions that fit like they were written by an internal operator. The difference was not intelligence. It was setup, and this creator explains it in a way that hits you in the gut.

Most entrepreneurs are still treating powerful AI models like simple chatbots, and it’s quietly costing them time, clarity, and momentum. I just finished watching a breakdown by a top business strategist who’s spent years in the AI trenches launching companies and building internal tools, and the message was simple: stop acting like a user, start acting like a director.

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The core idea is that AI shouldn’t live in “random question mode.” It should become your company’s creative operating system. The goal is to move from doing the work yourself to directing the work, and that only happens when you front-load context so the model understands your business deeply. The expert calls this “sequencing”: build the infrastructure once, then every future session gets faster, smarter, and more aligned.

Here are the three major takeaways on how to turn ChatGPT into a business engine:

Mastering Context and Identity
If AI output feels generic, it’s usually because the AI has no real picture of who you are, what you sell, who you serve, and what “good” looks like in your world. The author is blunt about a non-negotiable starting point: use the paid version so you have the speed and features that make this workflow practical.

Next comes the “Role Master Prompt,” basically a compact “CEO brain file” the AI can reference. The clever hack here is not writing it yourself, but asking ChatGPT to interview you: “Interview me to create a master prompt as the CEO of my company.” Let it ask about revenue, team size, products, customer type, constraints, and current bottlenecks, then answer freely (voice mode helps if you think better out loud). The AI turns the mess into a tight document you can save and reuse, so the model stops guessing and starts building on real context.

To make it stick, update your account’s Custom Instructions as your “always on” preferences. Tell it how you want answers shaped (no fluff, bullet points, 8th-grade reading level, tables, checklists), and you’ll stop wasting time repeating formatting rules every session. It’s a small tweak that pays rent every single day.

Engineering the Perfect Output with System Prompts
Once the AI knows your world, you need repeatable instructions for specific jobs. The expert frames system prompts as future intellectual property: the business value is not one great response, it’s a prompt that reliably produces great responses on demand.

The practical method is reverse engineering. Describe what you want, let the AI draft it, then refine it in a document until it’s exactly right. After that, ask: “Write a detailed system prompt that would have reliably generated this exact output.” Now you have a reusable blueprint, not a one-off win.

To supercharge results, the innovator lists seven keywords that act like levers:
Act as role (define a persona like copywriter, analyst, COO).
Deep research (aggregate, cross-check, verify instead of surface-level answers).
First principles (rebuild the solution from fundamentals, not assumptions).
Devil’s advocate (force risk analysis and failure points).
Constraints first (budget, time, tools, compliance, scope).
Format as (JSON, table, SOP, email, PDF-ready outline).
Verify and cite (reduce hallucinations, anchor claims to sources).

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Scaling Operations with Projects and Custom GPTs
The final piece is organization, because messy chats create messy thinking. The expert recommends using Projects to isolate context: one project per campaign, client, or initiative, with the relevant files living inside it (brand voice, winning emails, customer research). This creates “compounding context,” where the model gets smarter inside that lane instead of mixing signals from unrelated work.

Then comes delegation: productize what works by building Custom GPTs. Once you have a system prompt that consistently nails a task (book summaries, SOPs, outreach emails, meeting notes), lock it into a Custom GPT and share it with your team. That way, employees don’t need to be expert prompters, they just use the tool you built, and quality stays consistent because the method is baked in.

This approach can compress your growth timeline because you’re no longer reinventing your process every time you open a chat.

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