Vibe Coding vs. Spec-Driven Development in the Age of AI

AI coding has changed the geometry of things in software development. Before, the code was scarce, it was a costly bottleneck that made businesses think twice before building features. Now, prototypes that once required substantial engineering time can now take hours or days. So, everyone wins, right?

The things are a little more complicated. AI remains a tool capable of scaling good ideas fast – but bad ones as well. When the first wave of vibe-coded apps began to show basic security and reliability flaws, it became clear that quality software needs clearer direction than ever before. That’s where spec-driven development came in.

Let’s explore the two contrasting approaches in modern software development and see whether vibe coding is as bad as its critics claim, the spec-driven model is as flawless as it sounds, and how not to lose money with either.

Key takeaways

• AI has made producing software cheaper and faster, but the business consequences of building the wrong software remain the same, from damaged revenue to user harm.
• Vibe coding and spec-driven development are not competing approaches. They solve different problems and can form different stages of the same development process.
• Specifications are becoming an active part of the development system, not static artifacts. When kept up-to-date, they guide and validate AI implementation.
• The right level of specification depends on the cost of being wrong. High-risk software requires more control than low-cost experimentation.

What is “vibe coding”?

Vibe coding is a style of software development where software is built primarily through natural language instructions, focusing on overall intent and immediate visual or behavioral feedback rather than line-by-line code inspection. The user guides, refines, and iterates on the “vibe” and functionality of the output until reaching the desired result.

For decades, coding has been more of a private membership club, with engineers’ scarcity, dozens of programming languages, and pure magic behind the scenes. Vibe coding has made it accessible for a wider audience, regardless of their education, profession, or technical background. AI played its part here – in a few years it moved from a basic assistant for software engineers to a technology capable of building apps even from non-expert prompts. However, the results remain uneven.

But how do you really approach vibe coding? Different tools support parts of a vibe-coding workflow, but the choice does not, by itself, determine how carefully the code is reviewed.

Spec-driven development (SDD) is another delivery approach that emerged alongside AI-assisted coding. It puts the specification at the heart of software development, making it the source of truth for both developers and coding agents.

The key distinction between this method and more traditional, pre-AI practices (we obviously had specifications back then) lies in the active use of the spec, its machine-readable and actionable nature. Not “write the spec once and forget about it,” “update it only when something breaks,” or “keep it around as documentation”. Teams still have to correct ambiguities and update the documents as the software changes.

SDD tooling connects specifications, AI execution, validation, and version control into a single workflow. For example, instead of a basic prompt like “let customers view their invoices,” a feature spec explicitly defines access rules, API edge cases, and test requirements – giving AI clear execution boundaries and reviewers concrete criteria to verify.

Spec-driven development vs. vibe coding: key trade-offs

While there are many ways to compare the two approaches, we selected the differences that matter most to decision-makers.

Speed to feedback vs. confidence in behavior

A vibe is an intuitive feeling rather than a detailed plan. Vibe coding works the same way: an idea strikes, you prompt an AI, and within hours you have a working prototype. This accelerates early discovery – allowing teams to test interface options and user journeys before committing to formal planning. The trade-off shifts from development speed to system reliability.

In contrast, specification-driven development requires more effort before you start coding. Depending on the goal, specs can have different levels of detail, specificity, and completeness. SDD doesn’t mean “plan everything up front”. You define what you want to build, ensure alignment with requirements, and give AI a stable context to avoid prompt-by-prompt divergence.

Flexibility vs. predictability

What does vibe coding’s ability to get results fast mean for AI-assisted software development? The answer is simple: more iterations during the ideation stage. Vibe coding works well when you need to explore high-level architecture options, experiment with wireframe ideas, or test different user flows. You can create ten concepts, reject eight of them, and change direction without a major investment.

The spec-driven approach is less flexible, but neither is it static: here, changes take time and are carefully considered by the development team. Acting as a reference point, specifications give you more confidence about the consequences of changes to the project. At later stages of development, this becomes particularly useful – when software accretes dependencies, integrations, and business rules, improvisation becomes harder to keep in check.

Upfront effort vs. downstream risk

Vibe coding, with its low initial effort and high affordability, allows teams and individuals to get started easily, with little upfront planning. However, when used beyond its strengths, it can backfire. The resulting software may solve the wrong problem, have conflicting features, or lack security measures, leading to debugging, rework, or even a rebuild. Sometimes teams turn to vibe coding cleanup services to audit, refactor, and secure AI-generated code.

Clarifying what exactly you want to build demands effort. After all, you need at least a small document that includes the goal, scope, business logic, key requirements, features, and constraints (both operational and technical). This work pays off later: you reduce downstream uncertainty and give AI agents explicit boundaries. However, specs are no silver bullet: a bloated or outdated specification wastes time and can easily misdirect AI.

The shift to spec-driven development: 4 stages

While there are no official stages to describe the emergence of specification-driven development, we describe the story the way it has been unfolding – we witnessed it ourselves.

1. Traditional development

Before GenAI, software development had been complex and expensive. The team often planned before implementation, connected dots by interpreting ambiguous requirements, and translated specifications into working software. The codebase was the definitive record of what the software actually did.

At that time, specs were treated as instructions for human developers and varied greatly, often drifting from reality as development progressed depending on the team’s processes. Yet code couldn’t race ahead of documentation overnight. Each change had a human cost, limiting how quickly the implementation diverged from the spec – and making reviews and tests essential to catch those gaps.

2. Vibe coding

And then vibe coding arrived. The idea that anyone could now build software was captivating. The possibilities seemed endless, and the excitement was hard to resist. You no longer had to write code line by line; you could simply tell AI what you wanted and watch it take shape. But the euphoria came with a catch: generating code was easier than building good software.

The consequences weren’t long in coming – rushed products with security flaws, fragile foundations, and features that worked in a demo but failed in practice. It became clear that building quality software with AI requires more direction, clarity, and requirements.

3. Requirements-driven AI development

With AI, ambiguity becomes more visible, and mistakes can scale faster. The vibe-coding frenzy highlighted the core aspect of AI-driven development: intent must precede implementation. Requirements engineering is the first step toward consistent code generation. While they don’t prescribe every detail, they provide direction and some guardrails for the models to follow.

As requirements became the shared reference point for human-AI collaboration, team roles began changing as well. Developers and business analysts felt the major shift in responsibilities. For project managers and product owners, prioritization and defining success remained core responsibilities that became more important.

• Developers. The role shifts from writing code from scratch to guiding AI, reviewing generated code, and making architectural decisions.
• Business analysts. Still define business needs and document requirements, now also validate AI output for business meaning and priorities.
• QA engineers. Beyond executing test cases, they now spend more time on test strategy, coverage, risk analysis, and requirements validation.

4. Spec-driven development

In the age of agentic engineering, specifications ensure code stays true to intent over time. Acting as a reference point throughout development, the spec helps ensure that components work together as designed, features solve the problems they were intended to solve, and the overall solution behaves as expected. No duct tape needed.

Spec-driven development does not guarantee error-free code, just helps to align activities and identify issues before they aggravate. Specs can carry ambiguities and contradictions, and AI will turn them into working code. Moreover, vibe coding and specification-driven development aren’t competing methods. They can coexist and serve different purposes throughout the development process.

And then, of course, there’s money. Combined with AI, specifications make complex changes easier to assess across dependent components. Though detailed context increases upfront time and token usage, it prevents scope creep and costly rework downstream. Ultimately, specs control the cost of getting the right change, while vibe coding minimizes the effort to explore it.

How to implement spec-driven development

To make SDD an active reference for planning, implementation, testing, and change, you need to stick to key principles: timely updates, relevant level of detail, machine-readable format, and alignment over doc volume. Here’s how to make it work in practice.

Step 1. Define the business outcome

A wrong turn at agent speed is still a wrong turn.

Imagine you ask your AI coding assistant to create a dashboard for the customer support team. The agent builds you a dashboard with a polished design, clear visualizations, and integrations with other business systems. But it tells the support team nothing about which customers are at risk of churn, nor how to prevent it. That’s why business outcomes come first.

To prevent agents from building useless features, you should first decide the desired outcome and success criteria. The latter doesn’t always have to be a strict metric: depending on the use case, it can simply mean determining which of three approaches is feasible.

For this example, a more appropriate instruction would be:

“Create a dashboard for the customer support team that highlights customers at risk of churn and alerts the team when a customer’s risk crosses a defined threshold. Success criteria are X, Y, Z.”

The beauty here lies in the balance between specific expected results and the freedom for AI to make smaller decisions, like how to organize the dashboard or present the data.

Step 2. Capture requirements

The devil isn’t in the details you wrote down. It’s in the ones you didn’t.

When you start gathering requirements, the process can tempt you into defining every possible detail. That’s neither realistic nor necessary. The challenge is to define the pillars: business, functional, and non-functional requirements, then detail each to the extent the project requires.

Look for assumptions and gaps. Some things might be less obvious to an AI agent than to a human colleague, like what happens when input is missing, or an external service fails. Some things must be outlined explicitly by design: the assumptions affecting security, business rules, user permissions, or irreversible actions deserve explicit requirements or human confirmation.

There’s a fine line between completeness and exhaustiveness. Leave too much unsaid, and the agent makes decisions you never intended; specify every last detail, and you constrain it unnecessarily.

The risks aren’t hypothetical. In 2025, researchers linked exposure of sensitive data in apps built with Lovable to missing database access controls. The lesson is simple: if an app handles user data, access permissions and security rules aren’t optional implementation details – they’re requirements that must be made explicit and verified.

Step 3. Turn requirements into a specification

Good intentions don’t compile.

A specification turns requirements into expected behavior, dependencies, and acceptance criteria. This gives AI agents the clear context needed to implement software without guessing from scattered docs.

In SDD, specs act as both human documentation and machine context. They define what to build, how components interact, which constraints apply, and what counts as “done” – though even a well-written spec can still be incomplete or contradictory.

Specification validation helps catch issues before implementation. Teams review the spec for contradictions, missing decisions, and technical constraints, ensuring requirements match system capabilities. While an AI assistant can help spot these gaps, someone familiar with the product must resolve them.

At the specification stage, the right level of detail depends on complexity, risk, and how much room there is for a wrong interpretation. A good specification isn’t the longest; it’s the one that guides the decisions that matter. So, when is it time to stop? When the cost of further specification outweighs the risk of leaving a decision open.

Step 4. Break the specification into work

Give the agent a task, not a quest.

Small, manageable tasks move projects faster than monoliths that are difficult to reason about, implement, and verify. At this stage, decomposition and sequencing rule the day. You need to establish clear boundaries so the agent can work independently without guessing where its responsibility begins and ends.

The key activities include:

• Dividing large features into bounded tasks with explicit inputs, outputs, dependencies.
• Identifying which tasks can run in parallel and which depend on earlier decisions.
• Surfacing blockers and risks such as missing inputs before implementation begins.
• Assigning agents clear ownership of components and defining shared interfaces.
• Defining completion criteria so each task can be verified independently.

And again, the trick is to balance independent verification with coordination overhead. Tasks should be small enough to verify independently, but not so small that they create unnecessary overhead or fragment the architecture.

Cursor’s own experiments with multi-agent coding illustrate the coordination problem. In one early setup, a shared locking mechanism reduced 20 agents to the effective throughput of two or three because they spent much of their time waiting.

Step 5. Implement and validate against the specification

Don’t confuse “it works” with “it’s right”.

Once the spec is ready, make it part of the development process by giving agents the relevant instructions and context during implementation. SDD stands out because it becomes the starting point and a benchmark throughout the development. During implementation, it’s vital to review architecture, security, data integrity, and other high-impact decisions where mistakes are costly to reverse. Each output is compared with the given task, and code behavior is validated against acceptance criteria rather than by compilation or test results alone.

Once ready, feed the spec to your AI agents as context. The spec acts as a continuous benchmark throughout development. Beyond reviewing high-stakes decisions like architecture and security, teams must ensure tests cover the spec’s actual acceptance criteria and failure modes – not just basic compilation or surface-level execution.

Step 6. Keep the specification up to date

The spec shouldn’t belong in a museum.

Once you make spec-driven development the cornerstone of your AI-driven software process, your specification must evolve with the software. Whenever a requirement or design decision changes, the spec should be updated accordingly. When a change takes place, also track the consequences for dependent components, tasks, and tests.

A more advanced approach goes beyond reflecting code changes in the documentation: changes to the spec trigger corresponding code updates, with agents implementing and validating them. Such a workflow requires reliable AI agents, automated validation, and safeguards to keep changes under control.

Vibe coding vs. spec-driven development: when to use each

In AI-driven development, you have more than one way to get the job done. And choosing between vibe coding and spec-driven development isn’t always an either-or decision.

When vibe coding fits

While nearly 60% of people have an app idea, few ever build one – even as vibe coding lowers the barrier to entry. So, where does it fit best?

As a fast, low-friction approach, vibe coding works best with proof of concepts, prototypes, MVPs, and UI experiments. Here, if something fails, changing direction is cheap, and the consequences are minor. The main risk remains the same for vibe-coded apps – rushing a prototype to production software without necessary validation.

When used where more structure is needed, vibe coding can accelerate technical debt, leading to context drift, fragmented architecture, and rising maintenance costs. What starts as a shortcut can end up costing more.

When spec-driven development fits

When architectural complexity, strict security, or regulatory compliance matter, SDD is the right choice – especially for enterprise systems where multiple teams must stay aligned.

At first glance, SDD can resemble waterfall methodology, but defining specs does not require a complete design upfront or lock you into a rigid lifecycle. Teams refine specifications as they learn. However, applying detailed specs to low-risk tasks or throwaway prototypes creates unnecessary overhead and slows down experimentation before ideas are proven.

When to combine both

Vibe coding and SDD are not mutually exclusive – they coexist effectively when aligned with project goals. Use vibe coding where mistakes are cheap and rapid experimentation matters; switch to SDD where stability, edge cases, and maintainability are critical. The key challenge is recognizing when exploration has served its purpose and needs structural rigor to reach production quality.

Spec-driven development is still young. Most teams are working it out as they go: there’s no settled standard for what a good spec looks like, how detailed it should be, or how to keep it up to date with code that AI agents can now change in minutes. The tools are moving fast too, so a workflow that feels solid today may look dated in a couple of months. Teams that expect a finished, plug-and-play methodology will be disappointed.That doesn’t make it ineffective. For most projects that will reach real users, SDD is the most responsible way to use AI we have right now. A spec turns AI’s speed from a liability into an asset: it gives the agent a clear target, gives the team something to check the output against and leaves a record of why the software behaves the way it does. Without it, you are scaling guesswork.My advice is to adopt it now, keep it pragmatic and treat your own process as something that will evolve along with the practice.

Spec-driven development is still young. Most teams are working it out as they go: there’s no settled standard for what a good spec looks like, how detailed it should be, or how to keep it up to date with code that AI agents can now change in minutes. The tools are moving fast too, so a workflow that feels solid today may look dated in a couple of months. Teams that expect a finished, plug-and-play methodology will be disappointed.

That doesn’t make it ineffective. For most projects that will reach real users, SDD is the most responsible way to use AI we have right now. A spec turns AI’s speed from a liability into an asset: it gives the agent a clear target, gives the team something to check the output against and leaves a record of why the software behaves the way it does. Without it, you are scaling guesswork.

My advice is to adopt it now, keep it pragmatic and treat your own process as something that will evolve along with the practice.

Final thoughts

Vibe coding excels during early exploration, letting you quickly experiment with design and functionality. Moving to production with it alone, however, risks technical debt, inconsistency, and maintainability issues. Spec-driven development keeps teams and agents aligned, though overly detailed or vague specs create overhead without guaranteeing success.

Instead of picking a winner, choose the right tool for the project phase. While SDD consumes more context upfront, any initial savings from vibe coding quickly disappear in production through endless iterations and rework. At the end of the day, the right question is: “How cheaply and reliably can we get the right software into production?”, not “How can we minimize AI costs at every step?”