Intent Engineering: From Vibe Coding to Productive Outcomes

Introduction

Over Memorial Day weekend, I tried achieving flawless DSD-512 audio playback using an advanced setup (a powerful mini PC and an external GPU). I relied heavily on “Vibe Coding”—rapidly iterating with AI tools like ChatGPT. The speed felt amazing, but without guardrails, it quickly led me astray.

You might be skeptical of “Vibe Coding”—and I get it. Without structure, using AI to prototype rapidly can feel chaotic, leading us into countless rabbit holes:

• Following unvetted AI advice, I disabled my graphics card and ended up spending hours recovering basic functionality.
• I impulsively bought a $40 audiophile USB cable hoping for improvements that never materialized.
• Tweaking endless parameters and kernel flags buried me deeper into complexity, costing valuable weekend hours.

But dismissing AI-driven iteration entirely because of these misadventures would be throwing out the baby with the bathwater. The trick isn’t avoiding AI: it’s using it intentionally.

Why Rapid AI Iteration Often Goes Sideways

Rapid action feels empowering, but without intentional checkpoints, we often end up solving problems that don’t matter—or worse, creating new ones. Here are examples from my vibe coding experience this weekend:

Common Mistake	Real-world Example	Impact
Blindly following AI	Disabling the graphics card based on AI advice	2 hrs troubleshooting
Getting overwhelmed	Endless conflicting kernel settings	3 hrs lost productivity
Impulse purchasing	Unnecessary high-end cables	Wasted $40, no improvement
Stuck in short-term solutions	Windows hacks masking deeper kernel issues	Repeated problems over 4+ hrs

This kind of frantic activity without clear direction is like driving fast without GPS—you’ll only get lost more quickly.

Taming the Chaos: Intentionality and Structure

To help tame the chaos, I embrace Design in Practice, a structured method that breaks problem-solving into clear, manageable phases. I was first exposed to this practical problem-solving approach years ago while working adjacently to the Datomic / Clojure team.

This framework encourages pausing to think strategically—enough to keep focus without slowing momentum:

1. Describe: Clearly state symptoms and context.
2. Diagnose: Generate hypotheses and quick tests.
3. Delimit: Narrow down to a specific problem.
4. Direction: Define clear success criteria and constraints.
5. Design: Outline your solution approach.
6. Develop: Take the smallest practical action and validate immediately.

These phases encourage pausing to think strategically: just enough to avoid getting lost in constant action. I am not as structured about it as the practice suggests: I focus more on it as a way to document my current understanding of the problem and solution. It forces me to write things down, helping me use my peers as sounding boards and to clarify my own thinking.

Intent Engineering: Adding Intentionality to AI Iteration

As ChatGPT and other LLMs arrived on the scene, I naturally began experimenting with them through chat interfaces and an Obsidian-backed server setup. These tools extended my use of Design in Practice—previously limited to conversations with peers and personal notes—into a powerful, AI-augmented “Second Brain,” helping me document, clarify, and iterate my thinking and execution more intentionally and effectively.

Rather than jumping straight into rapid-fire experimentation (Vibe Coding) without guardrails, I blend fast iterations with structured reflection and checkpoints. I call this Intent Engineering, and it looks like this:

• Clarify Intent: Begin by clearly stating your goal in a single sentence. This keeps your efforts focused and aligned.
• Burst: Use AI to rapidly generate ideas, suggestions, or solutions related to your intent.
• Pause & Reflect: Take a moment to critically evaluate each idea—ask yourself: Is it necessary? Is it sufficient? Does it fit the strategic goal?
• Structured Pass: Apply the six-phase Design in Practice framework to ground and organize your best ideas before moving forward.
• Iterate: Alternate between quick AI-powered bursts and thoughtful pauses, cycling until your intent is fully realized.

Analogy: It’s like skiing downhill—speed can be exhilarating, but control and balance ensure you stay on the right trail.

Common Skepticism FAQ

• “Isn’t Vibe Coding reckless?”: Yes. When guided by intentional reflection, it becomes less so. It can even be an accelerator for creative problem-solving. The key is to balance speed with strategic pauses.
• “Why use ChatGPT/AI/LLMs if they give bad advice?”: You shouldn’t blindly trust AI—it’s a tool, not a decision-maker. Your design sense, judgment, nuance, and comfort challenging advice are more important than ever.
• “Won’t structured processes slow me down?”: Yes. That’s the point. Brief, deliberate pauses help you solve the right problems instead of the wrong ones.

From Prompt Engineering to Intent Engineering: An Evolutionary Journey

Early on, we learned Prompt Engineering, carefully crafting questions to get better results from AI models. Prompt Engineering alone, though, wasn’t enough to reliably solve nuanced, real-world problems.

We embraced a series of increasingly advanced—and often messy—approaches:

• RAG (Retrieval-Augmented Generation): Adding context from external documents improved responses but required tedious manual selection to avoid overwhelming the AI.
• Massive Context Windows: Trying to dump entire projects into AI’s memory —quickly became inefficient, costly, and impractical. Just as damning responses would quickly deteriorate in quality as the context grew too large.
• Graph-based approaches: Leveraging knowledge graphs showed promise but were operationally complex and difficult to manage, limiting their practical use.
• Chained Prompts and Automated Prompt Generation: Having AI generate or modify its own prompts streamlined certain tasks but risked losing strategic oversight and clarity.
• Vibe Coding: Rapid-fire, iterative experimentation with AI. It felt fast and freeing initially but quickly spiraled into unstructured chaos, leading to wasted time and misguided efforts.

Each of these steps provided valuable lessons, gradually shaping an understanding of how to integrate AI tools effectively:

• Prompts need careful thought.
• Context must be selective.
• Structure matters greatly.
• Automation requires oversight.
• Rapid iteration requires clear intent.
• The buck stops with you. AI is a tool, not an excuse for poor judgment.

Intent Engineering combines clear intent, rapid experimentation, structured checkpoints, and reflective evaluation. It acknowledges the limitations and strengths of each preceding method, balancing strategic thought with tactical execution.

Invitation to Skeptics: Try Intent Engineering for Yourself

You don’t need to commit fully right away. Here’s a simple exercise to get started with Intent Engineering:

• Pick one small task or challenge.
• Write down your intent clearly (one sentence).
• Use ChatGPT briefly to explore a few solutions.
• Stop after five minutes. Pick the best idea, document your rationale using the Design in Practice steps, then act.

Give it a try: you might discover, as I did, that AI combined with intentional pauses is an unstoppable combination for effective, efficient problem-solving.