There’s a cottage industry now around “prompt engineering.” Courses, certifications, job titles. People charging money to teach you the right way to talk to a language model.

Most of it is overcomplicating something simple.

The skill that matters isn’t knowing magic words or secret techniques. It’s the same skill that’s always mattered in any project involving other people (or now, machines) doing work for you: knowing what you actually want, and being able to articulate it clearly enough that someone else can build it.

That’s requirements gathering. We just gave it a fancier name.

The Real Bottleneck⌗

When someone gets bad output from an AI tool, they usually blame the AI. “It didn’t understand what I wanted.” “It gave me something generic.” “It hallucinated nonsense.”

Sometimes that’s true. Models have real limitations.

But more often, the problem is upstream. The prompt was vague. The constraints weren’t specified. The desired output format wasn’t clear. Edge cases weren’t mentioned. The human didn’t actually know what they wanted - they just knew they’d recognize it when they saw it.

Sound familiar? This is the exact failure mode of every bad software project since the invention of software.

“The developers built the wrong thing” is almost always “nobody wrote down what the right thing was.”

What Good Requirements Look Like⌗

Here’s what changes when you treat prompting as requirements gathering instead of magic incantations:

You specify the output format. Not “give me some ideas” but “give me a bulleted list of 5 options, each one sentence, focusing on X constraint.”

You state your constraints explicitly. Budget, timeline, technical limitations, things you’ve already tried that didn’t work, things you specifically don’t want.

You define success criteria. What makes one answer better than another? If you can’t articulate it, don’t expect the model to guess correctly.

You provide context that matters and skip context that doesn’t. The relevant background that shapes the problem. Not your life story.

You break big problems into smaller ones. If you can’t explain the whole thing clearly, you probably can’t build it either. That’s useful information.

None of this is specific to AI. It’s how you’d brief a contractor, a freelancer, or a new employee. The only difference is that the AI won’t ask clarifying questions by default - it’ll just guess and give you something. Which makes your clarity even more important, not less.

Why “Prompt Engineering” Gets Overcomplicated⌗

The prompt engineering industry has an incentive to make this seem harder than it is. You don’t pay for courses on obvious things.

So you get elaborate frameworks. Personas. Chain-of-thought templates. Role-playing scenarios. Specific phrasings that supposedly unlock hidden capabilities.

Some of this helps at the margins. Most of it is cargo culting. People copying patterns without understanding why they work, then teaching those patterns to others who copy them further.

The actual skill underneath all of it is boring: clear thinking translated into clear language.

If you can’t explain what you want to a smart person in plain English, you won’t be able to explain it to a model either. The model just makes the feedback loop faster. Bad input, bad output, retry - all in thirty seconds instead of three weeks.

The Skill That Transfers⌗

Here’s the useful reframe: every hour you spend getting better at prompting AI is actually time spent getting better at specifying what you want.

That skill transfers everywhere. Client briefs. Project specs. Delegation to employees. Even figuring out your own priorities. The people who are “good at AI” are mostly just people who already knew how to think clearly about problems and communicate requirements.

The people who struggle often struggled with those things before AI existed. They just had slower feedback loops that hid it.

The Uncomfortable Part⌗

This means the limiting factor isn’t the AI. It’s you.

If you’re getting bad output consistently, the model probably isn’t broken. Your prompts probably are. Which means your thinking about the problem probably is.

That’s not fun to hear. “The AI is dumb” is a more comfortable story than “I don’t actually know what I want.”

But the second story is fixable. You can get better at breaking down problems, specifying constraints, and articulating success criteria. You can’t really fix “the AI is just bad” without waiting for someone else to build a better model.

One of those puts you in control. The other doesn’t.

Practical Upshot⌗

Next time you get garbage output from an AI tool, before you retry with a “better prompt,” try this:

Write down what you wanted. Not what you asked for - what you actually wanted. Be specific. Include format, length, constraints, and what would make one answer better than another.

Then look at what you originally typed.

Most of the time, there’s a gap. The original prompt assumed the model would read your mind about things you never said. That’s not a prompting problem. That’s a requirements problem.

Close the gap. Be explicit. State your constraints like you’re briefing someone who’s competent but has zero context on your situation.

You’ll find the “prompt engineering” mostly takes care of itself.