Prompting the Machine: Why Asking Beats Telling in the Age of AI
“Progress isn’t made by those who command; it’s made by those who ask.” — John Michael Williams
A reflective paper on how curiosity builds better systems — and better people.
10-minute read
“Progress isn’t made by those who command; it’s made by those who ask.”
— John Michael Williams
About This Paper
In October 2025, a short screen recording captured what looked like a simple coding session — a command-line window, a few lines of code, and a scraper coming back to life.
But behind those commands lies something more profound: a philosophy about how we interact with machines, data, and ourselves.
This paper explores how shifting from telling to asking — from rigid instruction to curious inquiry — changes not only how we work with AI, but how we think, learn, and build resilience into every system we touch.
The example is technical: a scraper that crawls thousands of domains and updates a Google Sheet in real time.
The lesson is human: every restart is an opportunity to ask better questions.
Introduction: From Code to Conversation
When you type npm start, you’re not just executing a script — you’re beginning a dialogue.
In the recording, a scraper processes over 9,000 dealership domains, extracting contact data and syncing it live. Midway through, it stalls. The question becomes: can it pick up where it left off?
Most automation tools can’t. They start from scratch, forgetting what they’ve already learned.
That’s when the human operator — instead of commanding — begins to ask.
“Where did you stop?”
“What did you already finish?”
“Can we start from the next one?”
The machine answers.
The human listens.
Progress resumes.
This, at its core, is the philosophy of prompting — not just in AI, but in leadership, creativity, and code.
Research: The Anatomy of Asking
To understand why asking matters, we analyzed three types of interaction patterns — across both human and machine contexts.
Interaction Type Approach Typical Outcome Command-based (“Tell”) Direct, instructional, rigid Executes fast but fails quietly Assumptive (“Guess”) Predictive, assumes previous context Works intermittently, often resets Question-based (“Ask”) Reflective, iterative, data-informed Learns, adapts, and continues
In AI systems — and in human teams — the Ask approach consistently leads to higher recall, continuity, and trust.
The logic is simple: when you prompt with curiosity, you leave room for the system (or person) to respond, not just comply.
Barrier 1: The Habit of Telling
Humans are trained to issue commands.
Our workflows, leadership models, and educational systems reward decisiveness — not curiosity.
In coding, that looks like:
npm start
When it breaks, we run the same command again.
In management, that looks like:
“Just get it done.”
When it fails, we repeat the instruction louder.
But repetition isn’t progress.
Command without curiosity burns energy instead of generating insight.
In both AI and human systems, this “habit of telling” leads to fatigue, not flow.
Barrier 2: The Fear of Not Knowing
One reason people avoid asking questions — even to machines — is the discomfort of uncertainty.
Questions reveal what we don’t know.
Commands disguise it.
In prompting large language models or running automation scripts, admitting uncertainty (“I’m not sure where it stopped”) is the first step to intelligent continuation (“Can you show me where it stopped?”).
This vulnerability — in code, teams, or creative work — is where learning begins.
“The terminal is the only place where silence means thinking, not failure.”
— John Michael Williams
Opportunity: Asking as Architecture
When you ask, you architect resilience.
A question like:
grep “Processing:” scraper.log | tail -1
isn’t just a check — it’s a conversation with the past.
It says: Show me what I missed.
It bridges what’s done with what’s next.
This same logic powers effective AI prompting, project retrospectives, and team feedback loops.
Every system — mechanical or human — becomes stronger when it knows how to resume.
Barrier 3: The Illusion of Control
Telling feels like control.
Asking feels like surrender.
But in reality, curiosity creates control — because it replaces blind repetition with informed adaptation.
In the recording, the scraper didn’t need rebuilding; it just needed a better question.
That’s the same truth behind every successful AI deployment or campaign optimization:
we don’t need more force — we need better feedback.
Why This Matters: The Human Parallel
Automation isn’t replacing us — it’s mirroring us.
How we speak to machines reflects how we think about learning, failure, and time.
When you design systems that can ask before they act —
“Am I running correctly?”
“Do I already know this?”
“Should I resume or restart?” —
you’re building digital empathy into the process.
And when you, as the creator, pause to ask,
“What am I missing?”
“What’s this trying to teach me?”
“How can I continue instead of starting over?”
you’re practicing the same resilience you expect from your code.
Recommendations: Building the Culture of Curiosity
1. In AI Prompting
Don’t instruct, inquire.
Replace “Write a blog post about X” with
“What would a blog post about X look like if it were written to teach, not to sell?”
You’ll get depth instead of volume.
2. In Leadership
Ask your team:
“What made this hard?”
“What’s one thing you’d change?”
It builds trust faster than mandates ever could.
3. In System Design
Embed checkpoints for memory.
Every process should be able to ask: “Have I done this already?”
That single question saves thousands of re-runs.
4. In Learning
Encourage “debugging conversations.”
When something breaks, don’t ask “Who did this?”
Ask “What did this teach us?”
In the end, the video wasn’t about code — it was about curiosity. A simple command-line moment became a mirror for how we approach learning, leadership, and creativity. When the scraper stalled, the breakthrough didn’t come from force or speed, but from asking the right question: Where did I leave off? That single inquiry turned failure into continuation, proving that the best systems — like the best people — don’t start over when they stumble; they remember and resume.
The shift from telling to asking changes everything. It’s what separates automation from intelligence, process from progress. Whether you’re prompting an AI, debugging a script, or leading a team, the questions you ask define the quality of your outcomes. Telling commands attention; asking invites collaboration. It builds systems that listen, adapt, and evolve — a quiet kind of power rooted in understanding rather than control.
So, the next time you face a blank prompt — in a terminal, in your work, or in life — pause before you give instructions. Ask instead. Because the most meaningful progress doesn’t come from what we tell the world to do, but from what we’re willing to ask it to teach us. That’s how code becomes conversation, and how curiosity becomes the true operating system of change.
Written by:
John Michael Williams
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