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The Gap Between Requirements and Reality

Why detailed documentation doesn't prevent misalignment — and what I do instead.

For years, I wrote requirements.

Detailed ones. User stories with acceptance criteria. Process flows. Wireframes. Data dictionaries. I was thorough. I was careful.

And still, when the build came back, something was off.

Not wrong, exactly. Just... not what I pictured.

For a long time, I thought it was a communication problem. If I just wrote better requirements. If I added more detail. If I included more diagrams.

Then I realized something uncomfortable: the problem isn't the quality of the communication. It's the medium.

Words Are Interpretation Engines

When I write the word "fast," three people in the room imagine three different things.

The developer thinks response time in milliseconds.

The designer thinks fewer clicks to complete a task.

The stakeholder thinks delivery by next week.

All three are valid interpretations of "fast." None of them are wrong. But they're three different products.

Now multiply that by 200 requirements on a 6-month project.

Every single requirement is a word. Every word is an interpretation engine. Every interpretation is a fork in the road where the team might go left while the stakeholder imagined right.

This isn't a people problem. Developers build exactly what's described. Designers create exactly what's specified. The problem is that descriptions and specifications are made of words, and words are inherently ambiguous.

I spent years trying to solve this by writing better words.

It didn't work. Because the problem isn't the words. It's the gap between imagination and description.

The 40-Page Requirements Document

I once wrote a 40-page requirements document for a loan origination platform.

It had everything:

• 47 user stories with acceptance criteria
• 12 process flow diagrams
• Data dictionary with 200+ field definitions
• Wireframes for every screen
• Edge cases documented for every workflow

Six weeks later at the demo, the stakeholder said: "This isn't what I had in mind at all."

Not because the team was incompetent. Not because they didn't read the document. But because reading a 40-page document and imagining the same product I imagined when I wrote it — that's almost impossible.

The document was perfect. The product was wrong.

What Changed

I stopped trying to describe the product perfectly in words.

Instead, I started building rough prototypes and putting them in people's hands.

"Is this what you mean?"

"No, more like..."

"Like this?"

"Yes! But the button should..."

"Got it. How about now?"

That conversation — happening in front of a clickable prototype instead of a written document — closes the gap in minutes instead of weeks.

AI made this possible for me. Not because I learned to code. I still can't write JavaScript. But because I can describe what I want to AI the same way I'd write a user story, and get a working prototype back in minutes instead of weeks.

The gap between imagination and product didn't disappear. But the feedback loop went from 6 weeks to 6 minutes.

The New Process

The documentation still exists. But it follows the prototype, not the other way around. You document what works, not what you imagine might work.

This Isn't About Replacing Developers

Let me be clear about something.

Developers build production systems. Scalable architecture. Enterprise-grade software that handles millions of transactions. Security. Performance. Reliability.

That's a craft. A deep, complex craft. And it's not what I do.

What I do is test ideas. Quickly. Cheaply. Before committing a team's time and energy.

The prototype I build with AI in 2 days is not the production system. It's the conversation starter. It's the thing that replaces a 40-page document with a 5-minute demo.

Developers still build the real thing. They just build the right thing, because we validated it first.

What This Means for Delivery Practitioners

If you've ever felt that frustration — writing detailed requirements and still getting back something that's "not quite right" — the problem isn't you. And it's not the developers. It's the medium.

Words are ambiguous. Prototypes are concrete.

The skills you already have — understanding problems, defining success, testing assumptions — are exactly the skills AI needs from you.

You don't need to learn to code. You need to learn to describe what you want clearly enough that AI can build a rough version. And you already know how to do that. It's called writing user stories.