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AI can write thousands of lines of code in minutes. The harder question is whether it is building what you actually asked for. Spec-driven development is emerging as a more structured way to work with AI coding agents.
September 25, 2026
Imagine asking an AI coding agent to build a task management application.
You give it a simple prompt: "Build a modern task manager with user accounts, projects, deadlines and notifications."
A few minutes later, you have a working application.
The interface looks good. The buttons work. You can create tasks. The database is connected. Maybe there is even authentication.
It feels like magic.
Then you start using it seriously.
You discover that recurring tasks behave strangely. Notifications are triggered twice. A permission rule was interpreted differently from what you intended. One change breaks another feature.
This is one of the uncomfortable realities of AI-assisted development: AI can produce a convincing implementation even when the requirements were never clearly defined.
That is where spec-driven development enters the picture.
Spec-driven development, commonly called SDD, puts the specification at the centre of the development process.
Instead of starting with a vague prompt and immediately asking an AI agent to write code, you first describe what the software is supposed to do, what constraints it must follow and what conditions define a successful implementation.
That specification becomes a source of truth for both humans and AI agents.
GitHub's Spec Kit describes the approach as a shift toward making specifications more than temporary documentation. The specification defines intent and requirements before implementation, with AI agents using that structured context to generate or modify software. :contentReference[oaicite:0]{index=0}
The basic idea is simple:
Intent → Specification → Plan → Implementation → Verification
The code still matters. But the code is no longer the only thing that tells the team what the software is supposed to do.
Not really.
A detailed prompt can certainly improve the output of an AI coding agent. But a prompt is usually temporary. It exists inside a conversation and can easily disappear as the project grows.
A specification is intended to be a persistent project artifact.
It can live alongside the code, be reviewed, version-controlled and updated when requirements change.
This distinction becomes important when multiple people or multiple AI tools work on the same project.
One developer may use Cursor. Another may use Claude Code. Someone else may use Codex or GitHub Copilot. If everyone relies on their own conversation history, each tool can develop a slightly different understanding of the project.
A shared specification gives everyone the same reference point.
The prompt becomes the instruction for the current task. The specification becomes the longer-term contract.
The easiest way to understand SDD is to compare it with vibe coding.
Vibe coding often looks like this:
Idea → Prompt → Generated Code → Try It → Fix It → Repeat
This can be incredibly effective when you are exploring an idea or building a quick prototype.
But the process can become messy when the project grows.
Spec-driven development adds structure before implementation:
Idea → Requirements → Specification → Plan → Code → Tests → Review
The difference is not that one uses AI and the other does not. Both can use AI extensively.
The difference is where the source of truth lives.
In a loose AI workflow, the source of truth can become the conversation. In an SDD workflow, the specification is deliberately made persistent and reviewable.
A specification does not need to be a massive document that nobody wants to read.
In fact, many current SDD approaches encourage smaller, focused specifications rather than one enormous document containing every detail of an application.
A useful specification might describe:
The goal is not to document every possible detail. The goal is to remove ambiguity that could cause an AI agent or developer to make the wrong implementation decision.
One current open-source SDD framework recommends a useful rule: a specification entry should exist when removing it could materially change an implementation, review or verification decision. :contentReference[oaicite:1]{index=1}
This may sound strange at first.
If AI agents are becoming smarter, shouldn't we need less documentation?
In practice, the opposite can happen.
A more capable agent can do more things with less supervision. That is useful, but it also means that a vague instruction can produce a much larger incorrect change.
Traditional autocomplete might generate a wrong line of code.
An autonomous coding agent might interpret an unclear requirement, modify several files, create new dependencies, update tests and leave you with a beautifully implemented version of the wrong idea.
AWS describes modern coding agents as systems that can interpret goals, inspect development environments, generate or modify code and interact with tools such as IDEs and command-line interfaces. :contentReference[oaicite:2]{index=2}
The more action an agent can take, the more valuable clear boundaries become.
Let's return to the task management application.
Instead of telling an AI agent:
"Build a notification system for tasks."
A specification could say something like:
"Users should receive an email notification 24 hours before a task deadline. Notifications must only be sent for active tasks. Completed or cancelled tasks must not trigger notifications. A user should not receive duplicate notifications for the same task deadline."
Now the agent has something much more useful to work with.
The specification defines the expected behaviour and, just as importantly, the boundaries.
The developer can then ask the agent to create a technical plan based on the specification before allowing implementation.
That small pause can prevent a lot of unnecessary back-and-forth later.
A typical spec-driven workflow can look like this.
Start with the problem rather than the code.
What are you trying to solve? Who needs the feature? What should happen?
Turn the requirement into clear, testable statements.
This is where humans should catch ambiguity.
If two developers could interpret the requirement differently, the specification probably needs more work.
The agent analyses the specification and proposes how the feature should be implemented.
Only after the plan is understood does the agent modify the code.
Tests, checks and reviews determine whether the implementation actually satisfies the specification.
GitHub's Spec Kit documentation follows a similar philosophy, emphasising specification creation, refinement and implementation rather than jumping directly from a natural-language prompt to code. :contentReference[oaicite:3]{index=3}
There is another reason SDD is interesting for AI-assisted development: memory.
AI conversations are not always a reliable long-term record of why a technical decision was made.
A developer may spend hours discussing a feature with one AI tool, switch to another tool the next day and discover that the second agent knows nothing about the previous conversation.
A specification stored in the repository solves part of this problem.
The important project knowledge exists outside a particular chat session.
Some SDD frameworks take this idea further by placing small specification files close to the parts of the code they describe. This allows both humans and agents to access the relevant context without loading the entire project history into every interaction. :contentReference[oaicite:4]{index=4}
That can make AI-assisted development much more consistent as projects grow.
This is where the idea can easily go wrong.
If developers spend three weeks writing a 200-page specification before creating their first prototype, they have simply replaced one problem with another.
Good specifications should be useful enough to guide implementation without becoming a second codebase that nobody wants to maintain.
The practical approach is to start small.
Define the important behaviour. Identify constraints. Specify what success looks like. Build the feature. Learn where the specification was unclear and improve it.
Current SDD guidance also recommends starting with minimal specifications and expanding them when real implementation gaps appear. :contentReference[oaicite:5]{index=5}
Calling SDD the alternative to vibe coding does not mean vibe coding is useless.
There are situations where a quick prompt is exactly what you need.
If you are experimenting for an afternoon, creating a formal specification for every button is probably unnecessary.
The problem begins when a prototype becomes a real product but the development process never evolves beyond improvisation.
SDD becomes more useful as requirements, teams, dependencies and consequences become more complicated.
Spec-driven development changes what developers spend time doing.
Instead of immediately asking, "How do I implement this?", developers spend more time asking:
That may sound less exciting than watching an AI generate hundreds of lines of code in seconds.
But in production software, clarity often saves more time than raw typing speed.
The interesting part is that developers are not necessarily becoming less technical. Their technical work is moving toward requirements, architecture, evaluation and system-level thinking.
It is too early to say that one development methodology will replace all others.
But the underlying idea is becoming increasingly relevant as AI agents take on larger software tasks.
When AI only suggests a line of code, a vague prompt is relatively easy to correct.
When AI can modify an entire repository, run tests and make dozens of decisions on your behalf, the cost of ambiguity becomes much higher.
That is why the current wave of spec-driven development is interesting. It does not try to stop AI from writing code.
It tries to give AI something better to write code from.
The specification becomes the bridge between human intent and machine execution.
You do not need a complicated framework to experiment with the idea.
Take your next feature and create a simple specification before asking an AI agent to implement it.
Write down:
Then ask your coding agent to read the specification and create an implementation plan.
Review the plan before allowing the agent to write the code.
That one change can transform the interaction from "AI, build something like this" into "AI, implement this agreed-upon requirement."
Spec-Driven Development is a software development approach where a structured specification defines the intended behaviour, requirements and constraints before implementation. AI coding agents can then use that specification as a source of truth while planning and building the software.
No. Vibe coding generally starts with natural-language instructions and allows AI to determine much of the implementation. Spec-driven development introduces a more structured specification and review process before implementation. Both approaches can use AI coding tools.
No. Good specifications can be short and focused. The purpose is to clarify decisions that could materially affect implementation, testing or review rather than documenting every detail of the project.
Yes. SDD is particularly relevant to AI coding agents because a persistent specification gives the agent structured context and reduces the amount of intent it needs to infer from a short prompt or conversation history.
Not necessarily. Vibe coding can be useful for prototypes, experiments and small projects. Spec-driven workflows become more valuable when software has important requirements, multiple contributors, complex dependencies or higher reliability expectations.
The biggest problem with AI coding is not that AI cannot write enough code. It is that AI can write a lot of code before you realise it misunderstood what you wanted.
Spec-driven development tackles that problem by putting clarity before implementation.
Instead of relying entirely on prompts and conversation history, teams can create a persistent specification that explains what the software should do, what constraints matter and how success will be measured.
Vibe coding made software generation feel dramatically faster. Spec-driven development is an attempt to make that speed more predictable.
And as AI agents become capable of taking on larger engineering tasks, knowing what to build may become just as important as knowing how to build it.
Anas Khan
Technology and data science writer focused on making complex technical topics easier to understand.