With AI, you can let Jesus take the wheel. Just build the guardrails first.
That's the whole framework. Everything else is commentary.
But commentary is useful, so here it is.
The Most Expensive Mistake You'll Make
Going high-level too early will cost you more hours than any other mistake when building with AI.
I've been building websites, automations, and marketing systems with Claude Code for months. The promise of AI is leverage through abstraction. Describe what you want, let the model figure it out. And that promise is real. I've built things in hours that would have taken weeks.
But I've also burned entire afternoons watching a model confidently build the wrong thing.
Here's what happens. You give a high-level prompt: "Build me a landing page for a B2B SaaS product." The model gets to work. It picks a font. Chooses a color palette. Decides on spacing. Invents a copy style. Makes assumptions about button shapes, section layouts, mobile behavior, image treatment.
Each assumption seems reasonable in isolation. But assumptions compound. By the time you see the output, you're three layers deep in decisions you never made. The model built something. It's just not what you wanted.
So you iterate. You explain what's wrong. The model adjusts. But it's adjusting on top of a foundation you didn't define. Each fix creates new inconsistencies. Each iteration is plan, build, test. The time compounds.
Three iterations later, you've burned hours fixing a foundation you should have defined upfront.
The Counterintuitive Fix
To go high-level with AI, start low-level.
Front-load the primitives. The foundational decisions that everything else builds on. Lock them down before you start, and the model's assumptions become bounded by good defaults. It can still make decisions. It just can't drift far.
Compare these two prompts:
High-level prompt:
"Build me a landing page for a B2B SaaS product."
Primitives-first prompt:
"Build me a landing page. Inter font. 8px spacing grid. #1a1a1a and #ffffff only. 48px headlines, 18px body. Buttons are full-width on mobile, 200px on desktop, 12px border radius. Short punchy copy, no words over three syllables in headlines. One CTA per section. No stock photos."
Same outcome. Fraction of the iterations.
The second prompt isn't longer because I'm being pedantic. It's longer because I've learned which decisions compound. Font choice ripples through every text element. Color palette affects buttons, backgrounds, borders, hover states. Spacing creates rhythm or chaos. Copy style determines whether headlines get rewritten six times.
These aren't details. They're the decision space the model operates within. Define them early, and "build me a hero section" becomes a bounded problem instead of an open canvas.
Why This Works
AI models are remarkably good at execution within constraints. They struggle with unbounded creative decisions that depend on taste, context, or unstated preferences.
When you say "build me a landing page," you're asking the model to solve two problems simultaneously: what should this look like, and how do I build it. The first problem requires reading your mind. The second problem is what AI is actually good at.
Separating these problems changes everything.
You make the taste decisions. You define the design system, the component library, the copy rules. The model executes within those constraints. Its job becomes implementation, not invention.
This isn't about micromanaging the AI. It's about being clear on what matters so you can be hands-off on everything else.
Three Ways to Get There
Not everyone knows their primitives upfront. That's fine. There are multiple paths to the same destination.
Write the detailed prompt yourself
If you know your constraints, state them in a single message before the first build. Here are my primitives. Here's what I want. Go.
This works when you have a clear vision. You've built enough to know what you like. You can articulate the design system in your head.
I keep a running doc of my defaults. Fonts I use. Color combinations that work. Spacing systems. Copy rules. When I start a new project, I paste the relevant constraints and describe the outcome. The model does the rest.
Use AI to build your prompt
Describe the outcome at the highest level, then ask the model to interrogate you. Let it extract the specifics you haven't articulated yet.
"I want to build a landing page for my consulting business. Before we start, ask me questions to understand exactly what I want. Get specific on design, layout, copy style, and functionality."
The model becomes a creative director, pulling out decisions you didn't know you needed to make. Fonts? Colors? How many sections? What's the primary CTA? Mobile behavior?
Turn that conversation into your starting prompt. Now you have a detailed spec you didn't have to write from scratch.
Decompose first, compose later
Break your project into components. Build each one in isolation. Buttons, cards, sections, navigation, forms, copy blocks. Get each primitive exactly right before you combine them.
Once you have a library of locked components, go high-level. "Build me a pricing page using the components we've created." The model mixes and matches your building blocks. It can't introduce new assumptions because the vocabulary is already defined.
This approach takes longer upfront but pays dividends on complex projects. You're building a design system, not just a page. Every future build inherits the constraints you've already locked.
What This Means for the Future
The abstraction ceiling is rising fast.
Six months ago, high-level prompts failed constantly. Today, they work surprisingly often. The models are getting better at inferring intent, asking clarifying questions, and making reasonable assumptions.
This doesn't invalidate the framework. It changes where you apply it.
When models required constant hand-holding, you had to specify everything. As they improve, you can push the abstraction higher. The skill becomes reading the situation. When do you trust the model to figure it out? When do you get specific upfront to save three rounds of back-and-forth?
That's not a technical skill. It's judgment. Pattern recognition. Knowing what details matter enough to specify versus what to leave to the model. The people who've built real things have this taste already. AI just lets them leverage it.
The people who build fastest aren't the ones who write the longest prompts. They're the ones who know which constraints unlock leverage and which ones waste time.
The Skill That Matters
Everyone talks about prompt engineering like it's a discipline unto itself. Learn the magic words. Master the incantations. Unlock the AI's hidden potential.
That framing misses the point.
The skill isn't prompt engineering. It's knowing what to lock down so you don't have to steer.
Define the primitives. Describe the outcome. Then let go.
The model handles the rest. Not because you wrote a clever prompt. Because you constrained the decision space to the point where most paths lead somewhere good.
That's the leverage. Not working harder with AI. Working differently. Front-loading the thinking that matters so you can stay hands-off everywhere else.
Every hour you invest in learning these patterns compounds. The gap between people who know how to work with AI and people who don't grows wider every day.
Build the guardrails. Then let Jesus take the wheel.
Ready to Build with AI-Native Growth?
We've spent years building the guardrails that let AI amplify human judgment instead of replacing it.
Apply to work with us and get a growth system that knows when to go hands-off and when your input matters.
Robbie Jack•Jan 09, 2026•7 min read
