Spec-driven development vs. vibe coding: which path are you on?

Definition of vibe coding

The term “vibe coding” comes to life in early February, 2025, coined by Andrej Karpathy, an AI researcher and former OpenAI co-founder. According to the creator, vibe coding is designed as a low-stakes programming style for exploratory work and a relaxed approach to software engineering.

Basically, vibe coding is a practice of building software, websites, or applications by simply prompting an AI model in plain, conversational language. Instead of writing code line-by-line and debugging the program manually, you act as the visionary to describe what to build, guide an AI agent to do the heavy lifting, test the outcomes, and provide feedback. This flow iterates until the product functions to your liking.

At its core, vibe coding is a fundamental reassignment of roles. In the input-first development process, the developer stops being the person who writes the software. Rather than writing code directly, the developer becomes the person who wants it. Intent replaces implementation, and natural language replaces syntax.

How the vibe coding loop works

Vibe coding operates in a four-phase workflow:

• Phase 1 - Describe: This phase includes three baby plan steps. Before you start, you need to translate the messy idea in your head into a structured one: What you want to build. Then, define the intent: What you want the software to do regarding the purpose, behavior, and outcome. Next, you can design a prompt that tells the AI tool what to build using plain, conversational language, not formal system requirements. A prompt is not a specification, a syntax, or a technical document. It is literally a clear description where you simply articulate the vision of your future product in terms of what it is, what it should do, and how it should behave. For example, “build me a landing page with a sign-up form and a pricing table” or as detailed as a paragraph explaining the full behavior of a feature. The description is important as it shapes everything that follows in the next phase.
• Phase 2 - Generate: The AI model takes your description and does the heavy lifting. It interprets your intent and produces code. Depending on the complexity of the request, the AI model may produce a single function, an entire component, or multiple interconnected files in one pass. In Karpathy’s definition of true vibe coding, the developer does not review the what was produced. You trust the AI model’s judgment over your own scrutiny and “accept all.”
• Phase 3 - Review: In vibe coding, this phase does not mean auditing the code. Instead, you test the outcome. You run the application, observe its behavior, and assess whether it matches your predefined intent. If something breaks, you copy the error message and paste it directly back into the prompt. If the output is functional but incomplete, you describe the gap and note what is missing.
• Phase 4 - Iterate: Based on what the review revealed, you return to phase 1 and feed a new or refined description into the loop. You might be fixing a bug, adjusting a design, adding a feature, or correcting behavior that drifted from your original intent. The AI generates again. You test again. This cycle continues, and each brings the product closer to what you originally envisioned.

Where & when pure vibe coding works best

Vibe coding drastically lowers the barrier to entry for beginners and non-tech people while empowering experienced coders and engineers to rapidly prototype ideas, saving time on repetitive boilerplate code.

In reality, letting AI fully execute the code without human technical intervention works best in the following contexts:

• Greenfield projects & rapid prototyping: This is the strongest use case. When the goal is to test a new idea quickly, vibe coding compresses days of working into only hours. There is no existing codebase to integrate with, no legacy logic to navigate, and no inherited constraints to work around. The output just need to be functional enough to validate the concept.
• Hackathons & time crunches: When the priority is a working demonstration rather than a production-ready system, vibe coding compresses the distance between idea and output faster than any other approach.
• Single-purpose internal tools: Building simple, low-stakes applications used in low-risk environments or exclusively by a small team. For example, a basic analytics dashboard or an internal event registration form. If anything goes wrong, one single person can simply leverage AI tools to fix it without causing consequences.
• One-off scripts & automations: Tasks happening once, such as parsing a dataset, generating a report, reformatting a file, or automating a workflow that will never need to scale, are the contexts where vibe coding delivers value without unnecessary overhead or compromise.
• Non-technical founders & domain experts: Vibe coding closes the gap between idea and implementation for founders, designers, and domain experts who have a vision they cannot execute technically but an AI can.
• Applications built with standard tech stacks: Projects that rely on widely adopted, well-documented technologies (E.g., React, Node.js, Python, and other mainstream frameworks and languages) are suited for vibe coding. AI models have been extensively trained on these, so they can handle common tech stacks with high reliable speed. AI-generated code is more likely to be accurate and consistent with how experienced developers would write it.

Recommended tools for vibe coding

It is worth noting that vibe coding does not look the same across every tool or every developer. The vibe coding experience exists on a spectrum, from fully consumer-facing platforms designed for people who have never written code, to developer-native environments that integrate AI assistance directly into professional workflows. Where you sit on that spectrum directly influences your architectural decisions and how much the AI is trusted to fill the gap. The following tools, platforms, and workflows illustrate each tier.

• Tier 1 - Consumer-facing and no-code adjacent: Include platforms that require no programming knowledge or skill. A description in plain language is all it takes to generate a working application, complete with interface, database, and deployment. They handle technical decisions and infrastructure automatically. Suggested tools: Lovable, Bolt.new, Replit.
• Tier 2 - Developer-facing inline assistants: Act as intelligent pair programmers that integrate AI assistance directly into an existing IDE (Integrated development environment). These tools require programming knowledge and provide support for code extension, completion, and generation. The developer retains the ownership of the codebase and review what AI produces. Suggested tools: GitHub Copilot, Cursor, and Windsurf.
• Tier 3 - Agentic and terminal-based: This is the most powerful, developer-native tier in which vibe coding works at the project level. It means AI agents operate across the entire codebase, not just on a single file or function. The developer converses with the AI agent about the features or systems, and the agent navigates, edits, and generates across the full codebase autonomously. Suggested tools: Claude Code, Codex CLI, and Gemeni CLI.

Note that all three tiers support vibe coding. But they differ significantly in how much architectural control the developer retains.

Definition of the spec-driven development approach

Specification-driven development (SDD) refers to a development paradigm where the team first devises a highly-detailed specification before any code is generated. The written document called spec is a structured set of requirements, behavioral rules, edge cases, constraints, and acceptance criteria, defining exactly what the system should do, how it should be built, how it should function, and what conditions it must meet to be consider complete. From there, the team derives an implementation plan, breaks it into atomic tasks, and only then leverage AI agents to generate the code.

Spec-first or spec-anchored practice can be understood as context engineering applied to software development. Stored in the repository, the specification serves as a shared context that resolves ambiguities and aligns everyone involved, including developers, AI agents, and stakeholders, around a single, unambiguous definition of what is being built, while constraining and directing the AI agent’s output. Unlike in traditional development where codebase is the source of truth, detailed specifications in SDD are the authoritative foundation from which everything is built and all stakeholders work from.

In a spec-driven workflow, the specification is versioned alongside the code, making it a living documentation that evolves with the project. When requirements change, the spec is updated first and the relevant code is regenerated or modified to match.

How the SDD loop works

Unlike vibe coding, which begins with a prompt and figures out the details along the way, spec-driven development begins with a question: Do you know precisely enough what you want to build to write it down? The answer must be yes. And everything that follows is built on that foundation – the technical specification.

In the SDD loop, the spec-as-source does not replace AI agents; it directs them. Each phase is designed to keep the developer in control of intent and outcome while AI serves as an implementation engine.

Phase 1 - Specify

Everything begins with a specification. A well-formed document covers user stories that describe who needs what and why, functional requirements that define what the system must do, non-functional requirements that govern how it must behave, edge cases that anticipate failure modes and unexpected inputs, and acceptance criteria - the testable conditions that determine when each requirement is considered complete.

In practice, the specification is built around three core documents, including:

• A mission document that defines why the project exists, what its key goals are, and what is out of scope.
• A tech stack document that records the core technology choices, why they were made, and deployment decisions.
• A roadmap that outlines project phases, planned features, and milestones, updated continuously as the project evolves.

In SDD, this phase is the most important phase of the entire loop. The quality of everything that follows is directly determined by clarity and precision of the spec written here.

Phase 2 - Plan

In this phase, the developer and AI collaborate to produce an explicit plan before a single line of code is written.

By using the specification as the source of truth, the AI agent analyzes the requirements, works within the architectural constraints already defined, and breaks the implementation down into small steps, sub-tasks, and required files. The plan translates WHAT of the specification into HOW of the implementation; it answers the question every developer eventually faces: Given what we want to build, how exactly do we build it?

The developer reviews and validates the plan against the spec, challenges assumptions, and clarifies ambiguities before a single line of code is produced. If the plan contradicts the spec, or introduces architectural decisions the developer disagrees with, those conflicts are resolved right in this stage, not discovered later in a debugging session.

Phase 3 - Implement

With the plan reviewed, validated, and approved, the AI coding agent begins generating code. It works systematically through the sub-tasks and required files defined in the implementation plan. Every implementation decision is grounded in what the spec defines and what the plan prescribes.

The developer remains active throughout this phase. The developer monitors the output, review generated code against the plan, and intervene when the generated code diverges from the specification.

Phase 4 - Verify

This is where the SDD loop closes or restarts. The developer verifies the generated code against the acceptance criteria defined in Phase 1. This is not a subjective review but a direct, binary check: Does the system do what the specification said it must do?

Each requirement is tested against its acceptance criteria. Every edge case that was anticipated in the spec is deliberately triggered. Every non-functional requirement (e.g., performance, security, and reliability) is measured against the threshold that was defined. The output either meets the standard or it does not.

When it does, the feature is complete. When it does not, the loop returns to Phase 1 where the developer examines and refines the specification.

The failure is treated as a signal about the quality of the spec, not just the quality of the code. Each failed verification makes the specification more precise, and the refined spec produces more accurate output in every subsequent cycle.

Where & when spec-driven development works best

Investing in a specification before writing any code adds overhead that not every project justifies. But the spec-driven coding method “shines” when you need to build rigorous, predictable, and maintainable software systems utilizing AI-assisted coding agents.

• Production-ready applications: When real users depend on the system, SDD becomes the responsible choice. A well-written specification with explicit acceptance criteria defines what correct looks like so the output can be verified against it, not just shipped when it looks right.
• Multi-feature and complex systems: As a project grows beyond a single feature or a handful of files, the AI’s ability to maintain coherent context degrades. The SDD approach keeps the specification as a persistent, versioned source of truth, preventing decisions made in earlier sessions from being forgotten, contradicted, or duplicated across a growing codebase.
• Team-based development: SDD is inherently collaborative. The specification is a shared artifact that every team member can read from and build around. When a new developer joins the project, the spec provides the context they need without the original author having to re-explain every decision.
• Regulated & compliance-driven industries: For regulated teams, specifications are the only path through the compliance objectives their standards already require. Industries such as healthcare, finance, legal, and defense operate under frameworks that demand documented, auditable, and traceable requirements. A spec-driven workflow produces that documentation naturally as a living document that evolves alongside the software, not as an afterthought.
• Long-lived codebases: Systems that need to be maintained, extended, and handed off over time requires a foundation that survives beyond any single developer or session. The spec is that foundation.
• Security-sensitive applications: SDD embeds acceptance criteria and security constraints directly into the specification, giving the AI a behavioral contract that includes what the system must not do, not just what it should.
• Enterprise & cross-team projects: When multiple teams build interconnected systems simultaneously, unaligned assumptions compound silently until integration. And at the enterprise scale, the failures are expensive. SDD provides the single shared context that every team and every AI agent works from, ensuring complex business logic and service boundaries are defined before any code is written.

Recommended tools for spec-driven development

Adopting spec-driven development requires the right combination of tools. There are two groups of them - one is to manage the specification while the other is to execute it.

• Core spec-driven toolkits are those whose primary purpose is managing the specification itself: writing it, structuring it, versioning it, and driving the development workflow from it. The spec is the product of these tools. Examples: GitHub Spec Kit, AWS Kiro, BMAD-METHOD, Tessl, GSD.
• Orchestration & environment tools are the AI coding agents and IDE environments that execute the spec: reading it, generating code from it, and operating within the boundaries it defines. These tools support SDD but are not SDD tools by themselves. Examples: Claude Code, Cursor, GitHub Copilot, Windsurf, Gemini CLI.