Beyond Vibe Coding
From Simple Prompts to Spec-Driven Development
If you have started using AI to write code, you have probably felt the magic. You describe something in plain English, and suddenly it works.
"Build me a form." "Create an API." "Generate a dashboard." Within minutes, something is running. That is the thrill of what people now call vibe coding: building software through prompts, intuition, and rapid iteration.
It is powerful. It is fun. And for the right kind of work, it really does make you faster.
Why Vibe Coding Feels So Good
Vibe coding works especially well at the beginning. For a small tool, static website, simple app, or quick prototype, you do not always need a deep architecture plan. The model fills in the gaps, generates the first version, and gives you something tangible almost immediately.
That speed changes the emotional experience of building. Instead of staring at a blank editor, you are reacting to something that already exists. You can tweak, refine, and keep moving. It feels like you are building faster than ever.
Where Things Start to Break
The problem usually does not show up in the first prompt. It shows up later, when you try to extend what you built.
You add one feature. Then another. Then a third. Slowly, something starts to feel off. From my experience building i80agent, this is where the cracks appear.
Sometimes the data model does not hold up. It may be too simple, working for one case but breaking for the next. Or it may be too complex, making the system harder to understand and maintain than it needs to be.
Sometimes the system has no clear structure. Different prompts create different patterns, different assumptions, and different naming conventions. Each piece may work by itself, but the pieces do not really align.
Eventually, you stop building forward and start rewriting. You refactor core logic, redesign data structures, fix inconsistencies, and untangle decisions that were never made clearly in the first place.
The Real Problem Is Not AI
AI is not the real problem. In many cases, AI is doing exactly what we asked it to do. The issue is that the system was never clearly defined.
Vibe coding optimizes for getting something working. Real applications require something more durable: building something that keeps working as it grows.
That difference matters. A prompt can produce a feature. A well-defined system can absorb many features without collapsing under its own assumptions.
Do Not Rush to Code
One of the biggest shifts I have made is simple: do not start with code. Start with thinking.
As the system grows, the most valuable iteration should happen earlier in the process. Instead of iterating mainly on generated code, the better loop is to chat with the LLM, challenge the design, refine the specification, and only then generate or update the implementation.
In that sense, I still like the energy of vibe coding. The back-and-forth with the LLM is useful. The difference is where that energy goes. Instead of asking the LLM to generate code immediately, ask it to generate and refine specification files first.
Keep the conversation going until the spec feels right. Store the result in Markdown files, such as product requirements, technical design, data model, API contracts, UI flow, and implementation tasks. Then use those files as the source of truth for coding.
With AI, it is tempting to jump straight into implementation because the implementation is so fast. But the better approach is often closer to this:
Once the structure is clear, AI can implement very quickly. But if the structure is unclear, AI will still generate code. It just may not be the right code.
From Prompts to Spec-Driven Development
Instead of starting with "build me this feature," start by defining what the feature is supposed to do, how it fits into the system, what data it uses, how components interact, and what constraints exist.
In other words, move from prompting by feel to spec-driven development.
When I work with AI now, I try to begin with a specification. For each feature or system, I define the shape of the work before asking AI to implement it. The iteration happens in the conversation first: clarifying behavior, questioning assumptions, improving data structures, and tightening the design until the specification is strong enough to guide the code.
- Feature overview: what problem it solves and what users can do
- UI design: key screens and how they flow
- Technical overview: how the system should work at a high level
- API design: inputs, outputs, and contracts
- Data model: entities and relationships
- File structure: how components are organized
- Tech stack: what tools are used and why
- Hosting and deployment: how the system runs
This is what I mean by spec-driven development: not a heavy document for its own sake, but a clear, structured intent that gives AI the requirements, constraints, and acceptance criteria it needs before implementation begins.
These definitions become reusable templates. They give AI a better target and give me a better way to review what it produces.
Once the spec is good enough, I do not ask AI to build the entire system at once. I break the work into small tasks and implement them one at a time, checking each result against the spec before moving on.
Not Every App Needs the Same Depth
This does not mean every project needs a heavy specification. The level of detail should match the type of application.
For a simple website, the important decisions may be content, layout, navigation, visual style, hosting, and basic responsiveness.
For a SaaS application, the important decisions shift toward data models, API design, authentication, permissions, roles, workflows, and operational edge cases.
For an AI agent system like i80agent, the most important decisions include knowledge structure, retrieval logic, orchestration, confidence thresholds, fallback behavior, and decision rules.
The goal is not to over-engineer. The goal is to define the important decisions clearly.
AI Becomes More Powerful with Structure
When the specification is weak, AI fills gaps with assumptions. Outputs become inconsistent. The system becomes fragile.
When the specification is strong, components align. Outputs become more consistent. The system becomes easier to extend.
At that point, AI is no longer guessing. It is implementing.
A Simple Example
Instead of saying, "Generate an API for agent queries," define the request format, response format, retrieval behavior, LLM routing rules, direct-answer conditions, fallback behavior, and error cases.
Then ask AI to implement it.
The difference is significant: cleaner code, fewer rewrites, and a system that is easier to scale.
The Bottleneck Has Changed
It used to be: how fast can I write code?
Now the better question is: how clearly can I define the system?
That is a major shift. The value moves from typing syntax to shaping intent, architecture, constraints, and behavior.
Should You Stop Vibe Coding?
No. Vibe coding is still valuable. Use it for exploring ideas, building prototypes, testing interfaces, and learning new tools.
But do not rely on it as the foundation for systems that need to grow.
The real shift is from writing prompts to defining systems, and from vibe coding to spec-driven development.
Put another way: keep the conversational flow, but change the artifact. First vibe with the LLM on the spec. Then code from the spec, one task at a time.
Closing
From my experience building i80agent, AI can generate code quickly. But only a well-defined system can grow.
Vibe coding gets you started. Spec-driven development gets you to something that lasts.