Beyond Vibe Coding: A Guide to Spec-Driven Development

I’ve been spending a lot of time lately thinking about the future of software delivery, especially with the rise of AI. I’m in the process of writing a new DORA guide based on the seven AI capabilities defined in the DORA 2025 report, and a couple of them really stuck with me: “AI-accessible internal data” and “healthy data ecosystems.” How do we build systems where AI agents can reliably understand our intent and access the information they need to be effective partners in development?

This question led me down a rabbit hole and straight to Spec-Driven Development (SDD). It’s a structured methodology that feels like the perfect answer to creating that healthy data ecosystem. It moves us away from the chaotic, unpredictable world of “vibe coding”—where you’re just tweaking prompts and hoping for the best—and towards a world where our intentions are captured in a clear, machine-readable format. Think of it as your meticulously crafted coffee recipe, ensuring every cup—or every line of code—is consistently excellent, delightful, and exactly what your customers ordered.

The Problem with Vibe Coding: A Recipe for Disaster

Working with large language models (LLMs) can feel like a long, ephemeral Slack conversation. It’s great for brainstorming, but it’s a terrible way to build robust software. The non-deterministic nature of AI models creates a minefield of potential issues:

Non-Determinism: You run the same prompt twice and get two different results. It’s like ordering a latte and getting a flat white.
Forgetful Agents: In a long conversation, the agent forgets or ignores prior instructions, undoing previous work to satisfy the latest request.
Context Degradation & Rot: The quality of results degrades as the context window lengthens. This leads to context rot, where the AI loses track of the original intent. To combat this, developers often perform context pruning—manually trimming the conversation history—which is tedious and risks losing crucial information. The model also gets “lost in the middle,” struggling to access information that isn’t at the very beginning or end of the conversation.
Collateral Damage: An agent focused on one specific task might break other parts of the codebase without realizing it.
Hallucinations: The agent confidently makes up an API or assumes an incorrect library version based on stale training data.

SDD is the antidote to this chaos. It’s the difference between a rambling chat and a structured CloudFormation template.

What Exactly is Spec-Driven Development?

At its heart, SDD is about defining the what before diving into the how. It places the specification at the core of the development process, focusing on the intent of the code.

In a purist view of SDD, the specification becomes the long-lived source of truth. The code itself becomes a downstream artifact—disposable, regeneratable—while the spec is the core artifact you check into your repository. The goal is to make AI agents more accurate, predictable, and current, providing real validation that the generated code is trustworthy enough for production.

The SDD Workflow: From Recipe to Reality

SDD provides a reliable, traceable, and segmented development process.

Pre-Coding Planning (The Guardrails): The process starts by clearly defining the why, what, and how of a feature, with crystal-clear acceptance criteria and a definition of done.
The Implementation Plan: Before writing a single line of code, the agent creates an implementation plan. A human reviews this plan to ensure the agent’s understanding is correct. A successful plan almost always leads to successful execution.
Task Decomposition: Large features are split into smaller, discrete tasks designed to fit within a single context window. This keeps the agent focused and avoids context degradation.
Iterative Feedback on the Spec: Giving feedback on the requirements and design documents first is far more efficient than giving feedback on generated code.

This workflow can be supercharged with an Intent Integrity Chain (IIC), which guarantees the code adheres to the spec. The flow is: Formalized Requirements -> Machine-Parsable Specs -> Generated Tests. The LLM then iterates on the code until the tests pass, providing a verifiable guarantee of intent.

The SDD Toolkit: Your Barista’s Essentials

SDD relies on several key artifacts to provide structure and context to AI agents:

Markdown Specifications: Tasks and specs are stored as Markdown files in the Git repo. This gives the AI persistent context, overcoming its lack of memory. Tools like Backlog.md use front matter to store metadata for these tasks. Here’s a simple example of what one might look like:

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---
task_id: FEAT-101
title: "Implement user login endpoint"
status: "pending"
---
**Feature: User Login**

**As a:** user
**I want to:** log in with my email and password
**So that I can:** access my account

**Acceptance Criteria:**
- Given a correct email and password, the API should return a 200 OK with a JWT token.
- Given an incorrect password, the API should return a 401 Unauthorized.

Agent Steering and Guardrails: These are separate instructions that contain non-feature-specific guidance, like coding standards, tech stack choices, or rules to consult documentation. This puts the agent “on rails.”
Usage Specifications (Usage Specs): These are Markdown files describing how to use a specific library at a specific version, complete with example code. They are fed to the agent as context to combat hallucinations from stale training data.
Agent Hooks: These are automated tasks that run after certain steps, enforcing the Definition of Done by running tests, optimizing code, or updating documentation.

Several tools are emerging to support this workflow, including AWS Kira, Tessle, and Context Management Protocol (MCP) servers that provide agents with up-to-date documentation.

The Shifting Role: From Coder to Coffee Shop Architect

SDD fundamentally changes the developer’s role. You shift from being a primary producer of code to a manager, reviewer, and architect of the AI agent’s work. You focus on crafting the perfect “recipe” (the spec) and ensuring the final product meets the highest standards. Your primary creative output shifts from writing implementation code to designing robust, testable specifications and validating the results. You become the guardian of the system’s intent.

This approach also captures knowledge that is normally lost in chat sessions. The specifications help align humans and enable agents to learn from previous features, contributing to a project’s knowledge base.

Final Thoughts: Your Daily Dose of Development Excellence

Spec-Driven Development isn’t just a methodology; it’s a mindset. It’s about bringing precision, clarity, and a shared vision to AI-powered development. It’s about ensuring that every feature you ship is a perfectly brewed cup of software – robust, reliable, and satisfying.

So, next time you’re about to dive into a coding task with your AI assistant, pause. Grab a fresh cup of coffee. And ask yourself: “What’s my recipe for success here?”

What are your thoughts on Spec-Driven Development? Have you tried it? Share your brewing tips in the comments below!