From Vibe Coding to Spec-Driven Development: Why the AI Coding Paradigm Needs a Rework

1. When “Generate an MVP in One Prompt” Becomes a Collective Illusion

Over the past two years, the number of AI coding tools has grown almost as fast as the number of large language models. Developers now hold an ever-growing fleet of “assistants that write code” — and with it comes an illusion that has been proven wrong again and again: type a prompt of no more than a hundred words, go get a coffee, and come back to a fully running MVP demo.

Anyone who uses these tools heavily knows the price. Under the vibe coding paradigm, the most frequently typed instructions are things like “generate the complete, runnable code,” “that’s wrong, rewrite it,” “help me fix this part,” and “still throwing errors, take another look.”

These loops look efficient, but they hide a fundamental problem: when you push development forward by feel, what you are controlling is the pace — what you lose is certainty.

2. Three Structural Weaknesses of Vibe Coding

At its core, vibe coding is “going by feel.” It handles local tasks well: writing a single function, patching a regex, generating a data model. But once the task scales from “a local unit” up to “product-level features and technical architecture,” its limitations get amplified dramatically.

Weakness 1: Context drifts. As soon as the logic gets slightly more complex, the agent tends to drift away from the original intent, mixing irrelevant earlier conversation into its current judgment, or even over-interpreting a requirement. You end up guiding it step by step like a babysitter — interaction cost goes up instead of down.

Weakness 2: The decision chain is broken. This is the most damaging issue in team settings. The code got written, but “why it was written this way” cannot be traced. When a new member takes over, or when you come back to it three weeks later and face a pile of implementations with no stated rationale, the only “safe” option is to tear it all down and start over.

Weakness 3: Quality has no anchor. Without upfront constraints, every version the AI produces is a gamble. The bug you happened to fix today through harsh criticism may well reappear in another guise under a different context. Quality has no stable baseline; it only drifts randomly.

Together, these weaknesses point to one fact: vibe coding is good at “writing code,” but it cannot carry “software engineering.”

3. Where Writing Code Diverges from Software Engineering

To understand spec-driven development, you first need to clear up a distinction that has long been blurred.

“Writing code” is the easy part — it concerns syntax, snippets, and point implementations, and its correctness can be judged locally.

“Software engineering” has to be built on strict rules and quality control. It has to answer not just “is this code correct?” but:

• Where are the system boundaries? How do modules interact?
• Who guarantees data consistency? How are exception paths handled?
• Which higher-level goal does this implementation serve?

Put differently, code is generated, while architecture is “grown.” The latter requires constraints, requires context, and requires a set of rules that runs through everything; it cannot be compressed into a hundred-word prompt. Almost every claim that “one sentence generates a complete product” comes from someone who has never actually delivered a complete product with AI.

4. Spec-Driven Development: Moving Intent Forward into a Contract

It is against this background that spec-driven development was proposed as a paradigm. Its core idea can be stated in one sentence:

First define clearly “what to build” and “why it should be built that way,” then let the AI generate the implementation inside that closed space of rules.

If vibe coding is “get it running first,” spec-driven development is “write the contract clearly first, then hand it to the executor.” A spec is, essentially, a reusable and reviewable explicit contract — between human and machine, and among team members.

A reasonably complete spec usually covers three layers:

┌─────────────────────────────────────────┐
│  Requirements: user stories / acceptance  │
│  criteria / boundary conditions           │
├─────────────────────────────────────────┤
│  Design: module split / interface contracts│
│  / data flow                              │
├─────────────────────────────────────────┤
│  Constraints: coding conventions / tech    │
│  choices / prohibited practices            │
└─────────────────────────────────────────┘

The value of a spec lies in this: it externalizes the “implicit intent hidden inside prompts” into explicit, structured assets that can be version-controlled.

5. Vibe Coding vs. Spec-Driven Development

Dimension	Vibe Coding	Spec-Driven Development
Starting point	An ad-hoc prompt	Structured requirement and design specs
How it progresses	Trial and error by feel, repeated rewrites	Incremental generation within fixed constraints
Basis for decisions	Temporary, implicit, hard to trace	Explicit contract, reviewable and traceable
Quality assurance	Depends on after-the-fact criticism and random fixes	Upfront acceptance criteria plus rule constraints
Fit for complex scenarios	Weak, easily gets out of control	Strong, scales to system level
Team collaboration	Difficult	The spec is the documentation, so collaboration is natural

Worth noting: spec-driven development is not about discarding the immediacy of AI. It is about putting guardrails around it. The AI still handles efficient code generation, but the direction, boundaries, and quality standards of that generation are locked down in advance by the spec.

6. How to Put Spec-Driven Development into Practice

Even if your tools do not natively support a spec workflow, this methodology can still be implemented through engineering means. The core is to establish a closed loop of “rules — requirements — verification.”

Step 1: Build a project-level rule file. Write down the tech stack, directory structure, naming conventions, and prohibited practices as a standing rule document, and make it the context for every AI interaction. This is effectively a “project constitution” for the agent.

# Project Rules (example)
- Tech stack: backend language + framework, frontend component approach
- Directory convention: layer by functional domain, not by file type
- Prohibited: do not introduce unreviewed third-party dependencies
- Error handling: always use the project's wrapped exception types

Step 2: Write requirements as acceptance-ready specs, not one-liners. For every field and every interface, specify the input, output, boundaries, and exception branches. The more complete the information, the less freedom the AI has, and the more controllable the output.

# Requirement Spec (example)
## Feature: user registration
- Input: email, password (8-32 characters, must include letters and digits)
- Success: returns a user ID; email uniqueness check passes
- Failure: duplicate email / insufficient password strength / network timeout
- Acceptance: automated tests covering all of the branches above pass

Step 3: Close the loop with rules and verification. The spec defines “what to build,” the rules constrain “how to build it,” and automated tests are responsible for “whether it was built right.” Only when the three come together can AI output move from “a matter of luck” to “something measurable.”

7. Conclusion: Giving Engineering Certainty Back to Developers

AI coding tools are moving from “completing snippets” to “understanding intent,” and that trend is irreversible. But the stronger the tool, the more necessary it is for humans to define a clear foundation for it.

Vibe coding suits exploration, prototyping, and local tasks; any engineering work that faces production, team collaboration, or long-term maintenance should embrace spec-driven development. Its value is not just that it makes the AI make fewer mistakes — it puts the rationale for decisions, the baseline for quality, and the contract for collaboration back into the hands of developers.

Code can be generated by AI, but architecture must be led by humans. When the spec comes before the implementation, AI truly becomes a reliable engineering partner instead of a runaway horse that has to be watched at every turn.