How Spec-Driven Development Brings Structure to AI-Assisted Engineering and How We Put It to the Test

AI coding assistants have made developers incredibly fast since the start of the AI boom, but this new speed often comes at a hidden cost. The IT industry is realizing that generating code is the easy part. The real challenge is building systems that are coherent, maintainable, and actually do what they were supposed to do.

This is where Spec-Driven Development (SDD) comes in. This methodology shifts the focus from vibe coding to following the general intent, using specifications as the new source of truth for AI-assisted engineering.

Why Vibe Coding Isn’t Built to Last

The term “vibe coding” perfectly captures the current experimental phase of AI-assisted development. You describe what you want, get a block of code back, and if it looks right and seems to work, you move on. This approach is undeniably powerful for prototypes and small scripts, allowing for unprecedented velocity. However, when applied to serious, mission-critical applications, the cracks begin to show:

• The code might compile and even function, but the underlying architecture becomes an afterthought;
• New features create unexpected conflicts;
• Documentation is sparse or non-existent;
• The codebase transforms into a collection of disjointed components that are hard to maintain, debug, and evolve.

The problem isn’t the AI’s coding ability. It’s rather the workflow where developers treat AI like a search engine when they should be treating it like a literal-minded, but exceptionally talented, pair programmer who needs unambiguous instructions.

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What Is Spec-Driven Development

Spec-Driven Development is the practice of writing clear, structured, and testable specifications before a single line of code is generated. In the context of AI-assisted development, SDD provides the blueprint that guides AI agents to generate code that is consistent, architecturally sound, and perfectly aligned with business goals.

Unlike traditional waterfall requirements that gather dust, an SDD spec is a living, executable artifact. It becomes the shared source of truth for both humans and AI, driving development, software testing, validation, and even documentation. By moving architectural decisions, constraints, and clarity upstream, SDD directly addresses the shortcomings of vibe coding. It replaces guesswork with a clear contract for how your app should behave.

How Spec-Driven Development Works: A Step-by-Step Guide

The SDD process is structured into distinct, sequential phases. Each phase produces a key artifact that feeds into the next, ensuring a clear, traceable path from a high-level idea to production-ready code.

1. Specify (The “What” and “Why”). You start with a high-level description of what you’re building and why. The AI then generates a detailed functional specification. This phase is purely about business intent: user journeys, success criteria, and edge cases. It explicitly excludes technical details, forcing clarity on the problem before jumping to a solution;
2. Plan (The “How”). With the functional spec locked in, you provide the AI with your desired stack, architecture, and constraints. The AI generates a comprehensive technical plan, including technology choices, system design, integration patterns, and security considerations. This ensures the new code feels native to your project and aligns with your technical strategy;
3. Tasks (The Breakdown). The AI takes the spec and plan and breaks them down into small, reviewable, and actionable tasks. Each task is specific enough to be implemented and tested in isolation, like “create a user registration endpoint that validates email format.” This decomposition prevents the “big bang” coding approach that overwhelms both AI and reviewers;
4. Implement (The Execution). Finally, the AI tackles the tasks one by one. Instead of reviewing thousand-line code dumps, developers review focused changes that solve specific problems, verifying that the implementation matches the specification.

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A Quick Look at the Tools Enabling SDD

The SDD ecosystem is maturing rapidly. While GitHub’s Spec Kit is a powerful open-source example, other platforms offer different interpretations of the model:

Spec Kit

An open-source CLI and template-based toolkit that integrates with your existing AI assistants like Copilot, Claude Code, and Gemini CLI. It introduces the concept of a constitution.md, a file that encodes your project’s immutable principles, such as stack versions, naming conventions, and architectural patterns.

Kiro

An agentic AI with an IDE and CLI that add structure to an existing editor. Kiro has SDD built directly into its core. When starting a new feature, its agents automatically generate requirements, design documents, and create task lists, guiding the developer through an opinionated workflow. It’s designed for developers who want a deeply integrated, automated, and context-aware environment for moving from concept to code.

BMAD Method

An open-source framework that simulates an entire agile team using specialized AI agents. With over 12 distinct agent personas, including a Product Manager, Architect, Scrum Master, etc., it manages the entire project lifecycle.

XB Software’s Experience: Putting Spec Kit to the Test

Theory is one thing, but practice is where the real lessons are learned. Our team recently dove into GitHub’s Spec Kit to understand its practical applications and limitations. Here’s what we found trying to recreate the app shown below. It’s a small fleet management dashboard with a scheduler, a map, and vehicle tables:

Fleet Management System Demo

Our web development team started by testing the limits of the process. The first attempt was to generate a complete demo application, in one go. The high-level description was fed into the /specify command, and the results quickly revealed the AI’s contextual limitations. The application generated by Spec Kit was a mess:  the scheduler rendered strangely, filters didn’t work, and the statistics pulled random data not connected to the users or vehicles:

This shows that feeding too much information at once simply overwhelms the model. It gets confused and starts losing track of early requirements halfway through implementation.

Ujin Kosy

Functional Manager / Software Engineer

You shouldn’t feed a neural network a very large amount of data at once because it will get confused

The failure illustrated a core principle of SDD: decomposition is not optional. The AI’s context window, while large, has finite capacity. Handling a complex, multi-page application in one go leads to forgotten requirements and inconsistent results.

A better strategy is to build the project feature by feature. On the second attempt, our team started with the core layout (the header and collapsible menu) with a highly detailed spec that included exact styles and components:

Next came the “vehicles” page, specified down to the placement of inputs, instructing the AI to match the layout of a provided demo. While it wasn’t perfect, and the AI still decided to paint a button a different color, it was a manageable, high-quality chunk of work that could be easily corrected with a follow-up prompt or a quick manual tweak.

The final step was a tiny, well-scoped feature: adding sorting to two columns in an existing table. The simple command create client-sorting for type and year columns on Vehicle Stable proved perfectly suited for the SDD workflow, demonstrating that the methodology’s value extends to changes of any size, ensuring even small updates are implemented correctly and consistently:

Our experiments with Spec Kit revealed that successful AI-assisted development hinges on proper decomposition. Attempting to generate an entire application in one pass overwhelmed the model and produced unusable results, while breaking the project into small, well-scoped features consistently delivered high-quality, reviewable code. The key insight: specifications must match the size of the task, ensuring AI can maintain focus and consistency throughout implementation.

When SDD Makes the Most Sense

It is not a silver bullet for every coding task, but it provides immense value in specific scenarios:

• Enterprise & Production Systems. For long-lived applications where maintainability, stability, and compliance are critical;
• Complex Architectures. For projects with multiple services, APIs, and integration points where a lack of clarity can lead to catastrophic failure;
• Team Development. When multiple developers (and AI agents) need to collaborate on a shared codebase, a central source of truth is invaluable;
• Legacy Modernization. When rebuilding an old system, you can use SDD to capture the essential business logic in a modern spec before letting AI regenerate a clean, new implementation.

Conclusion: From Code-Centric to Spec-Centric Thinking

We are moving from an era where “code is the source of truth” to one where “intent is the source of truth.” AI is making specifications executable, turning our documented intent directly into working software. Spec-Driven Development allows small teams to build robust systems and large organizations to move with coherence and speed.

Have a project in mind? Contact us to discuss how we can help you harness AI-assisted development without sacrificing quality or control.