The Quiet Skill That Will Matter More Than Prompt Engineering

A friend showed me his "advanced prompting techniques" course. Hours of content on specific phrasings, chain-of-thought patterns, and formatting tricks to get better AI outputs.

Impressive technical depth. Completely missing the point.

Here's what I've learned from heavy AI use: the quality of your prompts matters far less than the quality of your problems. The skill that actually drives value isn't prompt engineering—it's problem framing. And almost nobody is talking about it.

The Prompt Engineering Obsession

The AI industry has created a cottage industry around prompt engineering. Courses, certifications, "secrets" that unlock AI potential. The implication: if you just phrase things right, AI becomes magical.

I'm not saying prompting doesn't matter. It does. But it's maybe 20% of the value equation. The other 80% is upstream—deciding what to ask in the first place.

Think of it this way: a perfect prompt for the wrong question gives you a perfect answer to something that doesn't matter. A mediocre prompt for the right question gets you 80% of the value. Where should you focus?

What Is Problem Framing?

Problem framing is the skill of understanding what you're actually trying to solve. It includes:

Defining the real problem. Not the surface symptom, but the underlying issue. "Sales are down" isn't a problem statement—it's a symptom. The problem might be product-market fit, messaging, targeting, or a dozen other things.

Identifying constraints. What's fixed and what's flexible? What resources exist? What must be true for any solution to work?

Understanding success criteria. How will you know if you've solved it? What does "good enough" look like?

Exploring the solution space. What categories of solutions exist? What's been tried? What assumptions limit thinking?

Problem framing happens before you ever talk to AI. It determines what conversations are worth having.

Why Framing Beats Prompting

Several dynamics make framing more valuable:

AI Can Optimize—But Can't Question

AI is excellent at optimization within frames. Give it a clear problem, and it generates solutions. But it can't step back and ask whether the problem itself is right. That's your job.

Prompts Will Commoditize—Problems Won't

Prompt libraries are everywhere. Best practices get documented and shared. The "secrets" of prompting become common knowledge quickly.

But problem framing is contextual and judgment-intensive. It doesn't reduce to templates. As I discussed in AI tools becoming commodities, the repeatable parts get automated—the judgment parts retain value.

Garbage In, Garbage Out

The oldest principle in computing applies: output quality depends on input quality. The most sophisticated prompt engineering can't save you from a poorly framed problem. Frame it wrong, and you're just getting wrong answers faster.

Problem Framing in Practice

Here's how I approach problems before touching AI:

The Five Whys (Plus One)

Ask "why" five times to drill from symptoms to root causes. Then ask "why this problem, now?" to understand context and urgency.

Example:

• "Our content isn't performing." Why?
• "Not enough traffic." Why?
• "Low search rankings." Why?
• "Content doesn't match search intent." Why?
• "We're writing what we want to say, not what they want to know." Why now?
• "Competition has improved, and we've stood still."

The real problem isn't "content isn't performing"—it's misalignment between content strategy and audience needs, made urgent by competitive pressure. Very different AI conversations follow from this reframe.

Invert the Problem

Instead of "how do we increase sales?", ask "what would guarantee we never make another sale?" Then prevent those things.

Inversion often reveals obvious blockers that direct approaches miss. It's also easier to identify what causes failure than what causes success.

Question the Goal

Is this the right goal? "We need to increase website traffic" might be wrong if the real objective is revenue. Sometimes solving the stated problem doesn't solve the actual need.

Define "Solved"

Before seeking solutions, specify what success looks like. Quantify where possible. "Better content" is vague; "content that ranks page-one for target keywords" is actionable.

This connects to what I track in my decision journal—clear success criteria make better decisions possible.

The Framing → Prompting Workflow

Here's how framing and prompting work together:

Step 1: Frame the problem (no AI)

• What's the real issue?
• What constraints exist?
• What does success look like?
• What categories of solutions are possible?

Step 2: Explore with AI

• Share your framing
• Ask for challenges to your assumptions
• Request alternative frames
• Generate options within your frame

Step 3: Evaluate and refine

• Does AI's output address the real problem?
• What's missing or wrong?
• Does the frame need adjustment?
• Loop back to Step 1 if needed

Most people jump straight to Step 2, optimizing prompts for a problem they haven't properly understood. The work in Step 1 makes everything else more effective.

Developing Problem Framing Skills

How do you get better at framing? Unlike prompting, there's no quick tutorial. But there are practices:

Study Frameworks

Mental models like first-principles thinking, systems thinking, and constraint analysis provide scaffolding for framing. Build a toolkit of frameworks to apply.

Practice on Real Problems

Take a current challenge and spend 30 minutes framing it before seeking solutions. Write out the problem statement, constraints, success criteria. Force yourself to articulate what you're actually solving.

Review Past Decisions

Look back at problems you "solved" that didn't stay solved. Often the issue was framing—you solved the wrong problem. These failures teach framing better than any course.

Seek Diverse Perspectives

Different people frame problems differently. Expose yourself to how others think about problems—especially those from different fields or backgrounds.

The Quiet Skill

Problem framing is a "quiet" skill because it's invisible. Nobody sees the thinking that happens before AI interaction. They only see the outputs.

This invisibility makes framing undervalued. Prompt engineering is visible and demonstrable. Framing is abstract and hard to show off. But the value is in the quiet work.

As I discussed in decision-making being the new skill, the thinking that happens before execution is where leverage lives.

The Bottom Line

Prompt engineering will be automated. AI will eventually figure out how to talk to itself optimally. The human value moves upstream—to deciding what problems deserve AI's attention.

Stop optimizing prompts for the wrong problems. Start investing in the skill of framing problems right.

The people who will thrive with AI aren't those who can phrase questions perfectly. They're those who know which questions are worth asking. That's the quiet skill that will matter—long after prompt engineering becomes trivial.

Want to ask better questions? Explore using ChatGPT as a thinking partner—the shift from answer-seeking to problem-exploration is where the real value lives.