Making AI Coding Controllable with OpenSpec: A Spec-First Path from Requirements to Shipped Code

The hardest part of AI coding is not speed. It is control.

When requirements are vague, AI fills in the blanks on its own. As discussions stretch across multiple rounds, the original goal starts to drift. After a feature goes live, there is often no reliable record explaining why it was built that way or where its boundaries are. In team settings, handoffs become even harder because the reasoning behind the code lives in scattered chat history rather than in something durable.

OpenSpec addresses that problem by changing the order of work. Instead of asking AI to write code immediately, it forces the requirement to be clarified first, locked into a specification, and then implemented against that specification. In that model, AI stops behaving like a guesser and starts acting like a precise executor.

At its core, OpenSpec is about making AI development controllable, traceable, and collaborative. Its value comes from three ideas working together: define the spec before implementation, align human and AI understanding in both directions, and keep a complete record throughout the development process.

Why spec-driven development fixes what goes wrong in AI coding

Most AI coding failures come from two sources: ambiguous requirement transfer and an unstructured workflow. OpenSpec turns fuzzy requests into explicit instructions so both the developer and the AI are working from the same source of truth.

Several common failure modes show up again and again:

• You ask for phone-number login and AI gives you email login.
• You want a small change and AI rewrites an entire file.
• After multiple rounds of edits, the implementation gradually moves away from the original goal.
• Once the feature is online, nobody can quickly answer why it was implemented this way.
• A new teammate takes over and has no easy way to reconstruct the feature’s context and evolution.

OpenSpec’s answer is not more prompting tricks. It is process structure.

Before coding starts, AI is guided to ask the missing questions and turn the request into a structured proposal. During iteration, the specification becomes the single source of truth, so changes are anchored instead of drifting. After delivery, proposals, design decisions, and test cases remain as a usable development record. For teams, the spec documents plus change history make it much easier to understand how a feature evolved.

What OpenSpec is designed to do

Its design philosophy is intentionally practical:

• Lightweight and non-intrusive: it does not require a heavy setup or force a new project structure.
• Native fit for AI tools: it works with mainstream tools such as Claude Code, Cursor, and GitHub Copilot, so developers do not have to abandon their existing environment.
• Built for evolving projects: it is especially suited to ongoing product iteration rather than only greenfield work.
• The spec is the documentation: structured specs are generated as part of development and evolve together with the code, reducing the usual problem of stale docs.

The closed loop: proposal, review, implementation, archive

OpenSpec’s workflow forms a four-step loop: proposal → review → implementation → archive. Every step revolves around the specification.

To make that concrete, consider a simple example: adding tags to a todo app.

Step 1: Draft the proposal before writing any code

The first goal is not to generate code. It is to force the requirement into clear shape.

You begin by installing and initializing OpenSpec:

   # 全局安装（需Node.js ≥20.19.0）
   npm install -g @fission-ai/openspec@latest
   # 进入项目目录初始化
   cd todo-app
   openspec init

During initialization, you choose the AI tool you want to work with, such as Claude Code. OpenSpec then creates an openspec directory inside the project, including project info files, spec directories, and change proposal directories.

Next, the project context is filled in. AI can inspect the codebase and populate openspec/project.md with details such as the tech stack, coding conventions, and existing feature modules. In the todo app example, that might include a stack like React + TypeScript + LocalStorage. This matters because AI performs better when it understands the project context instead of only the feature request.

Then you create a change proposal inside the AI tool:

   /openspec:proposal Add Tag Functionality for Todo Items

At this point, AI does not jump into implementation. It starts asking requirement questions instead. For a tagging feature, those questions might include:

• What operations should tags support: create, edit, delete, attach to todos?
• Should tags have color distinctions, and if so, how many colors?
• Can a single todo have multiple tags? Should tags support search and filtering?
• How should tag data be persisted? Local only, or synced to the cloud?
• How should existing todo items be handled? Should they receive a default tag automatically?

Once those details are answered, OpenSpec generates a structured proposal package, typically including:

• proposal.md: the motivation, core requirements, boundary scenarios, and backward-compatibility notes
• design.md: technical choices such as the tag data structure, storage approach, and core logic
• tasks.md: implementation tasks broken down into concrete units like updating the data model, building UI components, implementing filters, and writing tests
• specs/: incremental spec files describing the functional and technical requirements in precise terms

This is where OpenSpec first changes the nature of AI coding. The conversation stops being “please write this feature” and becomes “let’s define exactly what this feature is.”

Step 2: Review and align before implementation begins

A proposal is only useful if both the developer and the AI are aligned on it. That review step is what prevents expensive rework later.

The review should focus on a few things:

1. Requirement completeness: are all important scenarios covered? For example, if a tag is renamed, should every linked todo update accordingly?
2. Technical fit: does the solution match the current project? If tags are stored in LocalStorage, is that sufficient for the expected usage?
3. Boundary cases: have edge cases been considered, such as duplicate tag names or what happens to a tag when related todos are deleted?
4. Iteration and correction: if something is missing or wrong, you revise the proposal before implementation starts.

A revision might look like this:

   请修改提案：1. 标签颜色支持6种预设颜色，不支持自定义；2. 一个待办最多关联3个标签；3. 旧待办默认无标签，不自动添加。

OpenSpec also provides commands to help inspect and validate the proposal:

# 查看当前活跃提案
openspec list
# 验证提案格式正确性
openspec validate add-tag-functionality
# 查看提案详情（含任务清单与规范）
openspec show add-tag-functionality

This review phase matters because it is the point where ambiguity is still cheap to fix. Once AI starts coding, every unclear assumption becomes more expensive.

Step 3: Implement against the spec, not against memory

After the proposal is approved, implementation begins. The key difference is that AI now works from the task list and spec documents rather than from a loosely remembered chat history.

That brings several advantages:

1. Task-based execution: AI can move through tasks.md step by step, marking progress as it completes each item.
2. Consistency with the existing codebase: because project context has already been captured, generated code is more likely to match naming, structure, and style already in use.
3. Mid-course correction without chaos: if a design issue is discovered during implementation, you can pause, update the proposal, and continue from the revised spec.
4. Tests generated alongside the code: implementation is not just feature code; it can also include unit tests and manual test cases tied to the specification.

The article’s example data model for tags is as follows, generated according to the proposal’s structural rules:

// 按提案中的数据结构规范生成
export interface Tag {
  id: string; // 唯一标识，UUID生成
  name: string; // 标签名称，非空且不重复
  color: 'red' | 'blue' | 'green' | 'yellow' | 'purple' | 'orange'; // 预设6种颜色
  createdAt: number; // 创建时间戳
}

export interface TodoItem {
  id: string;
  title: string;
  completed: boolean;
  tagIds: string[]; // 关联标签ID，最多3个
  updatedAt: number;
}

// 存储工具按规范使用LocalStorage
export const tagStorage = {
  saveTags(tags: Tag[]): void {
    localStorage.setItem('todo_tags', JSON.stringify(tags));
  },
  getTags(): Tag[] {
    const data = localStorage.getItem('todo_tags');
    return data ? JSON.parse(data) : [];
  },
  // 其他方法按规范实现...
};

The point is not that this code is complicated. The point is that it was produced under constraints already agreed upon: a fixed set of six colors, no duplicate names, a maximum of three tags per todo, and LocalStorage as the persistence layer. That is what controllability looks like in practice.

Step 4: Archive the change and turn it into durable project knowledge

Once the feature is tested and accepted, OpenSpec closes the loop by archiving the change.

You can do that through the terminal:

   # 终端归档
   openspec archive add-tag-functionality --yes
   # 或在AI工具中执行
   /openspec:archive add-tag-functionality

After archiving, OpenSpec automatically handles several things:

• The change proposal is moved into an archive directory, typically date-based, such as 2025-10-20-add-tag-functionality
• Incremental specs are merged into the main project specs under openspec/specs/
• Spec consistency is validated to ensure there are no conflicts
• An archive report is generated, showing information such as the number of files added, files modified, completed tasks, and unfinished tasks

This step is what turns a one-off development session into a traceable project record. Instead of leaving behind only code, the project retains the requirement, design rationale, implementation breakdown, and testable boundaries.

Beyond a single feature: where OpenSpec becomes more valuable

OpenSpec is useful for straightforward feature development, but its value increases as the work becomes more complex.

Multi-module features

Consider a feature in an e-commerce app where the cart, coupon logic, and order flow all affect each other.

In that situation, OpenSpec can help by linking specifications across modules:

1. During the proposal phase, AI can identify the related modules and ask questions about coupling logic, such as whether a coupon only applies to certain product categories or whether changing cart contents should trigger coupon recalculation.
2. During spec design, it can define the cross-module rules clearly, for example that a coupon only becomes valid when the cart total reaches a certain threshold.
3. During implementation, tasks can be separated by module: data model updates first, UI interactions next, integration tests last.
4. During archiving, the specs for the cart, coupon, and order modules are all updated so the interaction logic remains traceable.

For work that crosses boundaries, a shared spec is often more important than the code itself.

Refactoring projects without losing control

Refactoring is one of the highest-risk uses of AI coding because the model may overreach, change too much, or break compatibility while trying to “improve” the code.

OpenSpec reduces that risk through spec validation:

1. In the proposal phase, you define the goal, scope, and compatibility requirements explicitly—for example, converting class components to function components, optimizing request performance, or limiting the refactor to a specific user module while keeping old APIs compatible for a period of time.
2. In review, AI can produce a before-and-after spec comparison to confirm that the refactor preserves behavior.
3. In implementation, the work is broken into small iterative steps, and each module is checked against the spec after completion.
4. In archiving, the project keeps both the spec changes and the relevant code history, making rollback and audit much easier.

This is a major improvement over asking AI to “refactor this codebase” and hoping it does not break anything important.

Team collaboration and governance

OpenSpec is also a response to a team problem, not just an individual developer problem.

In collaborative development:

1. Specs can be shared in the repository so the team maintains them together and AI prioritizes team standards when generating code.
2. Permissions can be controlled through Git workflows, which helps determine who can create proposals, review them, or archive completed changes.
3. Conflicts can be surfaced explicitly when multiple people modify the same spec area at the same time.
4. Review comments and revision history remain attached to the change files, creating a durable collaboration record.

The practical effect is that project knowledge no longer disappears into private prompting sessions.

Reusing specs for other assets

A strong spec does not have to end its life as internal documentation.

Archived OpenSpec documents can be reused to generate:

• automated tests, based on scenario descriptions in the spec, including frameworks like Jest or Cypress
• user-facing manuals, by extracting visible behavior such as the steps for creating tags or the conditions for using a coupon
• API documentation, by pulling parameter definitions and response formats into something like Swagger when the feature involves backend interfaces

That secondary use is important because it means the effort spent clarifying requirements pays off more than once.

Where OpenSpec fits—and where it does not

OpenSpec is not a universal solution. It works best when the cost of ambiguity is high enough to justify the extra structure.

It is highly suitable for:

• iterative development in existing projects
• complex features involving multiple modules or substantial business logic
• team collaboration
• projects that require long-term maintenance

It is a poor fit for:

• quick prototype validation from zero to one, where direct AI generation may be faster
• tiny one-off scripts, especially around 100 lines or less
• situations where the requirement itself is still directionless and has not been clarified at all

The pattern is simple: the more you care about control, maintainability, and shared understanding, the more OpenSpec makes sense.

How to keep the process efficient

A spec-driven workflow can become heavy if used carelessly, so a few habits matter.

Useful ways to speed it up

• Prepare a requirement checklist in advance, covering goals, user scenarios, and boundary conditions, so AI needs fewer rounds of questioning.
• In review, focus on core scenarios and edge cases first; not every minor detail needs the same level of scrutiny.
• During implementation, let AI batch simple tasks such as adding fields, but break complex work like core algorithms into smaller steps.
• Periodically clean up outdated or duplicate specs so the documentation stays concise and usable.

Common pitfalls to avoid

• Do not overdesign the spec. Focus on necessary conditions. Precise details are useful, but pointless elaboration creates drag.
• Do not iterate the proposal endlessly. Too many rounds can reintroduce requirement drift; keeping revisions within a small number is usually wiser.
• Do not archive before testing. Core user flows should be manually verified before a change is formalized.
• Do not treat the spec as static. It needs active maintenance to stay aligned with the codebase.

The larger shift: balancing AI speed with control

OpenSpec is not trying to restrain AI for its own sake. Its role is to make AI’s generative power land exactly where the project needs it.

That changes the developer’s role. Less time is spent repairing guessed code, and more time goes into requirement design, technical decisions, and business value. For teams, collaboration gets smoother because decisions are recorded instead of implied. For projects, quality becomes easier to control because the implementation remains tied to documented intent.

The shift is from “AI writes what it thinks you mean” to “AI builds what the spec actually says.”

That may not be the fastest possible way to generate code in the moment. But for ongoing projects, shared codebases, and work that needs to be maintained over time, it is a much more sustainable way to develop with AI.