Refactor, Part 2: ∆ 02. Question

I had data. Months of it.

Walking distances. Diet experiments that weren't right for what I wanted to achieve (building a Marvel comic character physique). Gym sessions that went nowhere. Weight fluctuations. Energy patterns. Sleep quality. Mood tracking. A growing collection of observations about what my body and mind were actually doing versus what I thought they were doing.

But data without direction is just noise.

This is ∆ 02. Question — the phase where you transform raw observation into focused intention. Where you stop asking "What's happening?" and start asking "What do I actually want to happen?"

It sounds simple. It's not.

The Trap of Vague Goals

My first attempts at goal-setting were disasters.

"Get healthy." "Get in shape." "Be a better father." "Stop the destructive patterns."

These weren't goals — they were wishes. And wishes don't give you anything to engineer toward.

The problem with vague goals is that they can't be measured, which means they can't be achieved systematically. You can't A/B test "get healthy." You can't iterate on "be better." You can't debug "get in shape."

As an engineer, I knew better. In code, if you can't define the expected behavior, you can't write tests for it. If you can't write tests, you can't verify that your solution works. If you can't verify, you're just guessing.

But somehow, when it came to my own transformation, I was guessing.

The Question Framework

The breakthrough came when I started applying the same rigor to goal-setting that I applied to system requirements at work.

Instead of "What do I want?" I started asking:

"What specific, measurable outcome do I want to achieve?" "By when?" "How will I know I've succeeded?" "What would success look like to someone observing from the outside?"

This forced me to get uncomfortable with specificity. Vague goals feel safe because you can't really fail at them. Specific goals are terrifying because failure becomes obvious.

But specific goals are also the only ones you can actually achieve.

The Real Questions

After months of data collection, I finally asked myself the right questions:

Physical: Do I want to be lighter or stronger? Do I want to look better in clothes or perform better in the gym? Do I want endurance or power? Do I want to be lean year-round or willing to bulk and cut?

Mental: Do I want discipline or flexibility? Do I want to eliminate all substances or learn moderation? Do I want rigid systems or adaptive frameworks?

Lifestyle: Do I want to be the guy who never misses a workout or the guy who can travel and adapt? Do I want to meal prep everything or learn to make better choices on the fly? Do I want to track everything forever or build intuition?

Timeline: Do I want dramatic change in 90 days or sustainable change over 2 years? Do I want to look good for summer or build something that lasts decades?

Each question forced me to choose. And choosing meant eliminating options. Which was scary. But also clarifying.

My Specific Questions

After months of observation and weeks of questioning, I landed on specific, measurable goals:

Primary Question: How do I build a lean, strong physique that I can maintain for decades without sacrificing my relationships or mental health?

Sub-questions:

• Can I get to 12% body fat while maintaining strength?
• Can I build a routine that works whether I'm home or traveling?
• Can I develop food relationships that don't require perfect adherence?
• Can I create systems that get stronger under stress rather than breaking?

Success Metrics:

• Visible abs year-round (measurable via photos, scale, physical measurements, and AI analysis of daily progress photos)
• Bench 2x bodyweight (performance benchmark)
• Miss fewer than 4 planned workouts per year (consistency metric)
• Maintain results through at least 2 major life disruptions (stress testing)

Suddenly, I had something to engineer toward.

The Power of Constraints

The best questions create constraints. And constraints, paradoxically, create freedom.

When I asked "How do I get in shape?" I had infinite options and no clear path. When I asked "How do I get to 12% body fat while benching 2x bodyweight within 18 months?" I had clear parameters to work within.

Constraints eliminate decision fatigue. They turn the overwhelming question of "What should I do?" into the manageable question of "What's the most efficient path to this specific outcome?"

They also make it obvious when you're off track. If my goal was vague, I could rationalize any behavior as "progress." With specific targets, there's no hiding from the data.

The Question Evolution

Here's what I learned: good questions evolve as you get better data.

My initial question was: "How do I lose weight?" After observation: "How do I lose fat while building muscle?" After more data: "How do I get to 12% body fat while maintaining strength?" After understanding lifestyle: "How do I build a lean, strong physique I can maintain for decades?"

Each iteration got more specific, more nuanced, more aligned with what I actually wanted versus what I thought I should want.

The questions got harder to answer, but they also got more valuable to solve.

The Testing Mindset

The best questions are testable questions.

Instead of "Will this diet work for me?" I asked "Will eating 2,200 calories with 250g protein result in 1-2 pounds of fat loss per week while maintaining my current strength levels?"

That's a question I could test. I could gather data. I could measure results. I could iterate based on evidence.

∆ 02. Question taught me that transformation isn't about finding the perfect plan — it's about asking questions specific enough that you can design experiments to answer them.

From Questions to Hypotheses

Once I had clear, specific, testable questions, the next phase became obvious: I needed to form hypotheses.

If my question was "How do I get to 12% body fat while maintaining strength?" then my hypothesis became "If I eat in a moderate deficit while following a strength-focused training program and prioritizing protein, I should lose fat while maintaining or gaining strength."

That hypothesis was specific enough to test. Measurable enough to validate. Clear enough to iterate on.

That's ∆ 03. Hypothesize — where questions become testable predictions about what will work.

Coming next: Refactor, Part 3: ∆ 03. Hypothesize — How I learned to turn specific questions into testable predictions, and why being wrong was the best thing that could happen.

This is Part 2 of the Refactor series. Read Part 1: ∆ 01. Observe to understand how data collection became the foundation of everything that followed.