"Build a full-stack app in a day!"
That’s the headline that kicked off the AI-dev gold rush. But after the hype settled, many were left with the same feeling: underwhelmed.
AI tools can scaffold a React app or generate a CRUD API, but when the task grows complex - when it demands architecture, continuity, or creative judgment-they lose their way. The explanation I've seen again and again is that AI "can’t code anything novel", it's only good for boilerplate.
The limitation isn’t in the AI. It’s in how we ask it to work.
I’m not a developer; I’ve never written a line of JavaScript by hand. Three months ago, I created my first JS file - in my spare time around a full-time day job. Yet I’m in the final stretch of a 500-file JS project built entirely by AI. What makes that possible isn’t coding skill—it’s a structured workflow I call Vision-First Development.
Beyond Autocomplete
In most workflows, the AI is treated like a world-class autocomplete. The developer writes code, and the AI helps fill in the gaps, generating snippets and handling repetition. That’s efficient for incremental progress, but it also means the system’s architecture emerges reactively, not deliberately. When a task grows complex, the AI is forced to infer intent, invent missing context, and make creative leaps on its own.
This is the root of the problem. AI coder pain comes from an unclear vision. When the goals, principles, or desired outcomes aren’t fully defined, every decision the AI makes carries the risk of misalignment and rework.
Phase 1: The Dialogue (Defining the "What")
Vision-First Development solves this by front-loading the most important work. Before a single line of code is written, I start with a conversation.
The goal of this dialogue is to create a shared understanding of the "ideal state"—a complete mental model of how a feature should behave, feel, and integrate with the wider system. It is a code-agnostic exploration of the problem. Sometimes, if the vision is clear, this takes ten minutes. Other times, it becomes a journey of discovery, using the AI as an expert consultant to evaluate trade-offs and find the optimal path.
By the end of this phase, the AI and I share a clear understanding of what "done" looks like, establishing a foundation that makes implementation predictable and aligned.
Phase 2: The Execution (Full-File, One-Shot Generation)
Once the ideal state is defined, implementation shifts from exploration to execution. My role becomes that of an architect, systematically limiting the AI's degrees of freedom to ensure its power is channeled directly toward the pre-defined vision. Two layers of documentation keep the process aligned:
The Constitutional Layer: This layer consists of prebuilt context documents - overarching project principles and coding standards included with every prompt. These documents act as architectural law, eliminating thousands of potential wrong paths upfront.
The Contractual Layer: Docblocks at the head of each file defining purpose, responsibility, and internal principles. This transforms each file into a defined, constrained problem.
With these guardrails in place, the AI takes full ownership of coding. It generates entire files in a single shot rather than completing snippets. This frictionless, one-shot generation is the key to velocity.
The Why
The payoff for this front-loaded investment in clarity is a blistering implementation speed. I can now output complete, complex features at a rate of one fully-coded file every 3 to 5 minutes.
For instance, the last two major updates I shipped were deep, structural refactors that went straight through the project's central files. These updates, which totaled 16 and 30 full files respectively, took just 4 hours and 10 hours to complete.
That’s a sustainable pace of 3-4 complex, fully-integrated files per hour. The source files, containing pure logic, often go through without a single fix. Even the debugging, primarily on test files, is handled entirely by the AI. I still can't read the code, but this system allows me to play to my strengths: designing and optimizing systems. The AI generates, debugs, and perfects itself, guided by a clear vision, at an industrial scale.
This methodology isn't just theory. I built my open-source tool, Context Slicer, using this exact process. It's a key part of my workflow for managing the very context these conversations depend on.
I'll let the results speak for themselves. Explore the live app, inspect the source code, and decide for yourself if this approach is real.
Live demo: contextslicer.web.app
Source Code: GitHub repo
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