Why Spec‑Driven Development Is the Core of Vibe Coding

I believe that instead of spending every day chasing various Vibe Coding tricks, the priority should be mastering Spec‑Driven Development.

Tools keep changing, but spec‑driven development does not.

It is sufficient to solve the vast majority of everyday programming issues.

Why do I continuously advocate spec‑driven development?

Because it naturally aligns with a programmer’s way of working.

Before writing code, a mature developer first analyzes requirements, designs a solution, and considers edge cases—activities that traditionally stay in the mind or a design document.

Now this process is turned into a collaborative Spec with AI, iteratively discussed, supplemented, and revised until the Spec is sufficiently refined.

This process is essentially design.

The Specs I co‑write with AI tend to be lengthy. For example, when handling a recent online bug I produced a Spec of over 700 lines and more than 2,000 characters. Below are excerpts:

Many wonder why the Spec needs to be so long.

Because AI does not understand your business.

AI lacks knowledge of which data must never be lost, which historical logic must remain compatible, which areas can be changed, and which should stay untouched.

These details must be supplied by you, so the Spec is not written in a single pass.

Instead, it emerges after dozens of back‑and‑forth discussions with AI.

During this process I continuously add information AI does not know, for example:

Data cannot be lost;

This is a B‑end system;

Data is persisted before subsequent processing;

Some historical logic must remain compatible;

…

At the same time I explicitly state constraints, such as:

Do not break existing code;

Minimize the scope of changes;

Do not introduce new issues;

Verify that the bug is truly resolved;

…

The overall design remains largely my decision. I tell AI the intended solution and let it analyze:

Any missing scenarios?

Better implementation options?

Potential risks?

Unconsidered boundary conditions?

Often the questions AI raises help uncover details I had not considered.

Interestingly, after dozens of rounds the resulting Spec itself becomes a well‑structured AI instruction.

After many rounds of discussion, the distilled Spec is a clean, formatted AI command.

Some people use AI merely for chat, adding requirements or questions as they arise, which eventually leads to a long, chaotic conversation log.

My approach is to continuously distill the consensus back into the Spec, ensuring:

Correct content is retained;

Missing scenarios are added;

Errors are corrected;

The outcome is not a sprawling chat transcript but an increasingly refined Spec.

AI does not care who authored the content; it cares about completeness, clear structure, and explicit constraints.

A polished Spec records not only requirements but also business context, design rationale, constraints, edge cases, and acceptance criteria.

Thus, in my workflow, chat is merely the process of forming the Spec; the Spec drives the AI’s work.

Is spending so much time writing a Spec wasteful? No.

The more complete the Spec, the higher the probability that AI generates correct code in a single pass.

Previously I wrote an article titled “A Thread‑Pool Online Fault Post‑Mortem: Four Defense Layers to Prevent Data Loss.” That bug, from analysis through design to code generation, was tackled around this Spec.

When the Spec was sufficiently refined, I let AI generate the full code in one go. My role was to review the code, deploy it to a test environment, verify the main flow, and then release it—without further iterative AI code modifications.

In the AI era, the truly valuable time is spent polishing the Spec, not writing code.

Many waste time repeatedly fixing AI‑generated code; I prefer investing time in perfecting the Spec.

When the Spec is complete, the subsequent coding phase becomes remarkably smooth.

Ultimately, the quality of AI output depends on the quality of information you provide. By continuously communicating with AI and refining a high‑quality Spec, AI can produce code that meets expectations efficiently and with high quality.

I believe this is the greatest “trick” of Vibe Coding.