TL:DR
Vibe coding isn’t powered by “vibes”—it’s powered by clarity.
The real differentiator in AI-assisted development isn’t the model or the prompt. It’s the combination of a strong instruction set (how the AI behaves) and a clear PRD (what you’re actually building).
Weak inputs create polished but misaligned outputs. Strong instruction sets and well-defined requirements turn AI from a guessing engine into a reliable builder.
The vibe is the interface.
The instruction set is the engine.
The PRD is the map.
People love the romance of vibe coding because it sounds like software can be built from instinct alone. You describe what you want, the AI fills in the blanks, and something useful appears. The term has become shorthand for building with natural language and AI assistance, though even some early discussion around it drew a distinction between general AI-assisted programming and true “vibe coding.”
That distinction matters because it points to the real engine underneath the workflow. It is not the vibe. It is the instruction set. In traditional product work, a PRD exists to define the purpose, scope, behavior, and success criteria of what is being built so that everyone is aiming at the same target. That remains true even in lighter-weight agile environments, where the format may change but the need for shared understanding does not.
The more time I spend working with AI, the less I believe great output comes from clever one-off prompts. It comes from upstream clarity. It comes from defining the role the model should play, the rules it should follow, the constraints it must respect, and the outcome it is supposed to produce.
That is why I keep coming back to the same idea: the instruction set is the AI’s DNA.
When most people think about prompting, they think about asking better questions. That matters, but it is only the surface layer. An instruction set is more than a prompt. It is the operating model for the system. It tells the AI how to behave before the task even begins.
A strong instruction set usually defines things like:
- role and perspective
- thinking framework
- communication style
- boundaries and guardrails
- output structure
- definition of done
Without that structure, the model does what models do: it fills in gaps with probabilities. It guesses. It generalizes. It borrows the most statistically likely version of the answer from its training and context window. Sometimes that works. Often it does not. The output may sound polished while being directionally wrong.
That is the hidden problem with weak vibe coding. It feels fast because the model generates a lot, but velocity without direction is just expensive wandering.
In my own chats, I have repeatedly found that the best results do not come from “just build this” prompting. They come from role assignment, context layering, task decomposition, and explicit constraints. In other words, they come from product thinking.
That is where the PRD enters the picture.
If the instruction set defines how the AI should operate, the PRD defines what it is actually trying to build.
Those are not the same thing.
The instruction set might say:
Act like a senior product engineer. Be precise. Surface assumptions. Mark speculation clearly. Break work into phases. Optimize for maintainability over novelty.
The PRD, on the other hand, says:
We are building a lightweight onboarding flow for first-time users. The target user is non-technical. The primary success metric is completion rate. V1 excludes social login, advanced personalization, and multi-language support.
One governs behavior. The other governs intent.
This is the part I think many people miss when they talk about vibe coding. They act as if the whole magic is in the model. It is not. The model is a capable engine, but the instruction set and the PRD are what give it a direction, a lane, and a standard for success.
A vague request produces vague software.
A detailed PRD produces coherent software.
A disciplined instruction set produces consistent software.
Together, they produce usable software.
That is why I increasingly see the instruction set as a kind of behavioral PRD for the AI itself. It is the product spec for how your collaborator should function. And if you are using AI to code, then your actual PRD becomes even more important than it was in traditional development, because ambiguity compounds faster when the machine can generate ten pages of code before you notice the premise was wrong.
Human engineers usually push back when requirements are fuzzy. They ask questions. They hesitate. They spot edge cases. An AI system will often do the opposite. It will complete the pattern. It will confidently create screens, endpoints, schemas, components, and logic based on assumptions that nobody explicitly approved.
That is why bad vibe coding feels magical for twenty minutes and disastrous two hours later.
The issue is rarely that the model “cannot code.” The issue is that it was never given a real product frame.
I have also learned that guardrails matter more than people think. Clean-room sessions, project separation, session resets, and tagged context are not just obsessive workflow habits. They are practical ways to prevent contamination. When AI starts carrying assumptions from one project into another, the output becomes erratic. One moment it is building for a consumer app, the next it is solving for an enterprise workflow that belongs somewhere else entirely.
People often describe that as randomness. I think it is usually state pollution.
A disciplined instruction set fixes that.
It says: here is the role, here is the stack, here is the audience, here is the tone, here is what is in scope, here is what is out of scope, and here is how to reason when things are unclear.
That is not bureaucracy. That is leverage.
I have used a framework in my own work that essentially follows this sequence:
- deconstruct the request
- diagnose the unknowns and dependencies
- develop within defined constraints
- deliver in a form that can be tested and used
That workflow sounds simple, but it changes everything. It forces the AI out of improvisation mode and into execution mode.
And that, to me, is the real story behind successful vibe coding.
The “vibe” is not the absence of rigor. It is the feeling of speed that happens when rigor has already been embedded upstream.
The smoother the interaction feels, the more likely it is that someone has already done the hard work of defining the system well.
This is also why I do not think PRDs are becoming less important in the age of AI. I think they are becoming more important, even if they get shorter, lighter, or more conversational. You can call it a PRD, a spec, a build brief, an instruction map, or a system prompt. The label matters less than the function.
The function is alignment.
What are we building?
Who is it for?
What problem does it solve?
What constraints matter?
What tradeoffs are acceptable?
What does success look like?
What is explicitly not part of this version?
If those questions are unanswered, the AI will answer them for you. That is usually not a gift.
For solo builders, this means the fastest way to improve AI output is not chasing the perfect prompt. It is writing a better brief.
For teams, it means the most valuable asset is not the single clever interaction with a chatbot. It is the reusable instruction set and the reusable PRD that multiple people, models, and agents can work from.
That is where scale comes from.
A shared instruction set creates consistency across outputs.
A shared PRD creates consistency across decisions.
Together, they turn AI from a novelty into an actual production tool.
So when I hear people celebrate vibe coding as if it is pure intuition, I think they are only seeing the surface. The best AI-driven building does not come from vibes alone. It comes from clarity disguised as conversation.
The future of vibe coding will not belong to the people with the loosest prompts. It will belong to the people who know how to encode product thinking, constraints, user needs, edge cases, and quality standards into language the machine can act on.
The vibe is the interface.
The instruction set is the engine.
And the PRD is still the map.
