I Tested OpenAI's 200 Prompt Templates—They're Useless. Here Are 12 That Save 2-10 Hours Per Week

OpenAI’s new prompt pack is so bad I feel like I have to write this guide.

And I’m not kidding when I say I worry about teams following cheap-and-easy guides like the new 200 prompt pack and getting fired. That’s how bad the prompts are.

But this is part of a larger pattern.

I’ve been watching teams waste their AI potential for months now, and it’s been frustrating. Not because people are lazy or uninterested—but because they’re being told that AI adoption is like rolling out Slack or Zoom. Learn the basics, tick the box, move on.

When OpenAI released their enterprise prompt pack last week, I saw it as the perfect example of what’s going wrong with AI education. Two hundred prompts that treat AI like it’s just better Google search. Generic templates that miss the entire point of what this technology can do.

So I built an alternative. Twelve workflow-driven prompts that show what happens when you start with the actual pain points in your work instead of starting with AI capabilities.

This isn’t a hit piece on OpenAI. They’re building incredible technology. But someone needs to say clearly: if you think AI education is one-and-done, you’re going to wake up in 2026 wondering why you’re left behind while others are 10x more productive.

I care about this because I see the gap. I work with teams that are sitting on 80-90% of untouched AI opportunity. They’re using ChatGPT for emails while their engineers grind through 4-hour code reviews and their sales teams make gut-based pipeline calls. The capability is there. The understanding of how to use it isn’t.

This post is my attempt to fix that. To show you what workflow-first AI education actually looks like. And to give you twelve working examples you can customize and use today.

Here’s what you’re getting:

• The core problem with current AI education – why treating AI like one-and-done software adoption will leave your team behind as capabilities advance exponentially

• The workflow-first methodology – a step-by-step approach to building AI into your actual work processes (not just “using AI more”)

• 12 production-ready prompts by job family:

  • 3 engineering prompts (technical debt prioritization, incident analysis, API reviews)

  • 2 product prompts (requirements extraction, competitive analysis)

  • 2 sales prompts (pipeline risk assessment, discovery prep)

  • 2 marketing prompts (content gap analysis, campaign briefs)

  • 2 customer success prompts (account health assessment, onboarding plans)

  • 1 operations prompt (budget variance analysis)

• Real examples of the difference – side-by-side comparisons showing why longer, context-rich prompts save 2-10 hours per week while generic templates waste time

• Customization framework – how to take these prompts and adapt them for your specific industry, process, and constraints

• Implementation guidance – what to do this week to start building workflow-first AI into your team’s actual work

• The continuous learning approach – why AI education never ends and how to keep pace with exponential capability growth

Each prompt is built to solve a specific workflow problem where teams currently waste hours grinding without results. They’re longer than OpenAI’s templates because they can do more. They include your context, your process, your constraints, and they’re designed to evolve as AI capabilities advance.

The difference between generic prompts and workflow-first prompts is the difference between getting analysis you could have gotten from Google in 2019 and getting decisions you can act on today.

If you’re serious about AI adoption—not the checkbox kind, but the kind that actually transforms how your team works—this is for you.

Let’s get into it.