Vibe Coding vs Spec Coding: How Should You Write Code in the AI Era?

What Is Vibe Coding?

In February 2025, AI researcher Andrej Karpathy coined the term Vibe Coding to describe a workflow where developers describe desired outcomes in natural language, let AI generate code, run it without reading, and iteratively fix errors by feeding back error messages until the program appears to work.

Key characteristics include: describing requirements in plain language, ignoring implementation details, accepting generated code based on runtime results, and using AI to repair failures. This approach thrives in personal projects, prototypes, and hackathons, with examples of weekend SaaS prototypes and 48‑hour app launches. The term was even selected as a 2025 dictionary word, indicating its cultural impact.

What Is Spec Coding?

Spec Coding (specification‑driven programming) is not new, but AI tools have amplified its practice. The core idea is “write the specification first, then write the code.” Developers first produce a product or technical spec that defines user stories, interfaces, data structures, and edge cases, then feed this spec to AI, which generates code strictly according to it. The resulting code is validated against the spec.

Tools such as Claude Projects, Cursor Rules, and GitHub Spec Kit make Spec Coding more actionable by allowing long‑term context for architecture, coding standards, and API contracts. Practitioners treat the spec as a living artifact that drives implementation, testing, and verification.

Data and Research: The Cost of Vibe Coding

Multiple studies and industry reports highlight risks of pure Vibe Coding for serious product work. One study found that on complex codebases, developers using conversational AI were about 19% slower than without AI, and the AI‑generated code was approximately 47% larger . Another analysis reported that AI‑assisted code introduced about 41% more defects , 322% more privilege‑escalation paths , and 153% more design‑related issues , leading to higher vulnerability rates. These figures illustrate that without clear specifications and acceptance criteria, the “describe‑then‑accept” model can amplify quality and security risks.

Vibe Coding’s strengths are rapid start‑up, low barrier, and suitability for exploration, but as code volume and logical complexity grow, developers who do not read the code struggle to debug and refactor, leading to technical debt and hidden security/compliance problems.

Spec Coding Advantages and Costs

The primary advantage of Spec Coding is that it clarifies what to do and how to do it before AI involvement, aligning both human and AI around a single source of truth.

Benefits include more reliable code structure and quality, better maintainability, a spec that serves as a communication and hand‑off artifact, clear acceptance criteria for AI output enabling automated testing, early exposure of design flaws during spec authoring, and easier scaling in collaborative, long‑term projects.

The trade‑off is the upfront time required to write a thorough spec and the need for strong system‑design and abstraction skills. In highly exploratory phases with vague requirements, extensive spec work may be inefficient. Moreover, if the spec contains errors or inconsistencies, AI will faithfully reproduce them, so specs must be iterated and reviewed.

Choosing the Right Approach

Scenarios where Vibe Coding fits best:

Personal projects, side projects, hackathons, or internal demos.

Rapid idea validation where production‑grade quality is not required.

Very fuzzy requirements where a runnable prototype guides direction.

Non‑technical founders building MVPs or one‑off scripts/automation tools.

Scenarios where Spec Coding is preferable:

Formal production projects that need long‑term maintenance and iteration.

Team‑based development requiring unified interfaces, data models, and workflows.

Domains with explicit security or compliance mandates (e.g., finance, healthcare).

Projects with substantial technical debt that require refactoring or large‑scale redesign.

A Simple Comparison: Building a Login System

Both approaches aim to create a user login system, but their processes differ markedly.

Vibe Coding example: Prompt the AI with “Create a React + Node login supporting email/password.” The AI returns runnable code in minutes, but it may store passwords in plain text, lack rate limiting, and issue non‑expiring tokens—issues that are hard to spot without reading the code.

Spec Coding example: First write a spec detailing the login endpoint (e.g., POST /api/login with parameters and response format), password hashing with bcrypt, JWT expiration of 7 days, and lockout after five failed attempts for 30 minutes. The AI then implements the spec, and each component is verified against the document. Although spec authoring adds an hour or two, the resulting code is production‑ready and easier to audit and extend.

Trend: Convergence of the Two Methods

Vibe Coding and Spec Coding are not mutually exclusive. An increasing number of developers adopt a hybrid “ Vibe exploration, Spec landing ” strategy: use Vibe Coding to quickly prototype core flows and validate ideas, then switch to Spec Coding for disciplined implementation, refactoring, and maintenance.

Tooling is evolving as well. Open‑source projects like GitHub Spec Kit support the “spec‑first, implementation‑later” workflow, while long‑context models in Cursor and Claude Projects make it easier to embed specifications and standards into AI context, even allowing the AI to infer an initial spec from a conversation and refine it.

The envisioned future state is an AI that can both “Vibe” when speed is needed and “Spec” when stability is required.

Summary and Recommendations

Vibe Coding lowers the barrier to creation in the AI era and excels at rapid exploration, but for maintainable, collaborative, production‑grade products, the discipline of Spec Coding remains essential.

Rather than idolizing a single method, the key is to recognize when to employ “vibe‑driven” rapid prototyping and when to switch to “spec‑driven” rigorous development. Judging the right moment for speed versus stability, for writing specifications versus merely running code, is the long‑term competitive advantage.