What Is Spec-Driven Development (SDD) for AI Coding Assistants?

Mar 27, 2026

The Problem

I asked an AI assistant to “add a login feature” to my app. It generated 200 lines of code in seconds. I reviewed it, found three issues, requested changes, and the cycle repeated. After five iterations, I had working code—but I also had technical debt, inconsistent patterns, and a vague feeling that the solution was more complex than needed.

Here’s the pattern I noticed:

User: "Add feature X"
AI:   [generates code]
User: [reviews, finds issues]
User: "Change this part"
AI:   [regenerates code]
User: [reviews, finds more issues]
→ Cycle repeats, quality degrades

The core problem isn’t that AI writes bad code. It’s that AI writes code without understanding my design intent. Each iteration adds complexity. Each fix potentially introduces new issues. The code works, but does it match what I actually wanted?

What Is Spec-Driven Development?

Spec-Driven Development (SDD) is a methodology where you write detailed specification documents before any code is generated. Instead of describing requirements directly to an AI assistant, you first create formal specs that define exactly what the code should do.

The philosophy is simple:

Think clearly before acting, use documents to constrain behavior, use verification to ensure correctness.

Here’s how the workflow changes:

TRADITIONAL:
Requirements → AI Code → Review → Fix → (repeat)

SDD:
Requirements → Specifications → AI Code → Verify against specs → Done

The key difference: specifications act as a contract between you and the AI. They constrain what the AI can produce and give you a baseline for verification.

Why This Matters

As AI coding assistants become more powerful, “writing code” becomes less of a challenge. The real difficulty is “writing correct code”—code that matches intent, follows design decisions, and covers all scenarios.

When I skip specifications, I encounter these problems:

Scope creep: AI adds features I didn’t ask for because it seems “helpful”

Missing edge cases: AI handles the happy path but misses error scenarios

Inconsistent patterns: AI makes different design choices in different parts of the code

Unclear requirements: Ambiguity in my request leads to wrong implementations

Each of these costs time in review cycles and creates technical debt.

How SDD Works in Practice

Let me show you a concrete example. I want to add user authentication to my application.

Without SDD

Me: "Add user authentication with JWT"

AI: [generates 300 lines of auth code]

Me: [reviews]
- No refresh token rotation
- Password hashing uses outdated algorithm
- No rate limiting on login endpoint
- Session handling inconsistent with existing patterns

Me: "Fix these issues"

AI: [regenerates code]

Me: [reviews again]
- New issues introduced
- Some fixes incomplete
- Code structure changed unexpectedly

With SDD

First, I generate specifications:

# User Authentication Proposal

## Goals
- Secure JWT-based authentication
- Compatible with existing session patterns
- Handle common attack vectors

## Non-Goals
- OAuth integration (separate feature)
- Passwordless authentication

## Success Criteria
- Users can register, login, logout
- Tokens expire and refresh correctly
- Failed login attempts are rate-limited

Then a design document:

# Authentication Design

## Architecture Decision
Use short-lived access tokens (15 min) with long-lived refresh tokens (7 days).

## Token Strategy
- Access token: JWT with user ID, roles, expiration
- Refresh token: Opaque token stored in database
- Rotation: New refresh token issued on every refresh

## Security Measures
- Password hashing: bcrypt with cost factor 12
- Rate limiting: 5 attempts per 15 minutes per IP
- Token blacklisting for logout

Then detailed specifications:

# Authentication Specifications

## Register Endpoint

### Request
- POST /auth/register
- Body: { email, password, name }

### Validation
- email: valid email format, not already registered
- password: minimum 12 characters, at least one uppercase, one number
- name: 2-50 characters

### Response
- 201: { userId, message }
- 400: { error: "validation failed", fields: [...] }
- 409: { error: "email already exists" }

### Side Effects
- Hash password with bcrypt
- Create user record
- Send verification email (async)

## Login Endpoint

### Request
- POST /auth/login
- Body: { email, password }

### Success Response
- 200: { accessToken, refreshToken, expiresIn }

### Failure Response
- 401: { error: "invalid credentials" }
- 429: { error: "too many attempts", retryAfter }

### Side Effects
- Verify password hash
- Generate access token (15 min expiry)
- Generate refresh token (7 day expiry)
- Store refresh token in database
- Record login attempt for rate limiting

Finally, a task breakdown:

# Implementation Tasks

- [ ] Create User model with email, passwordHash, name
- [ ] Implement password hashing with bcrypt
- [ ] Create register endpoint with validation
- [ ] Create login endpoint with rate limiting
- [ ] Implement JWT generation and validation
- [ ] Create refresh token mechanism
- [ ] Add token blacklisting for logout
- [ ] Write unit tests for each endpoint
- [ ] Add integration tests for auth flows

Now the AI implements each task against the specs. When I review, I check:

Does the implementation match the spec?
Are all requirements covered?
Do tests validate the specified behavior?

This verification step is key. I’m not reviewing code for correctness in the abstract—I’m checking against a concrete specification I already approved.

The Benefits I’ve Seen

Since adopting SDD, I’ve noticed several improvements:

Fewer review cycles: Specifications clarify requirements upfront, reducing back-and-forth

Better traceability: I can trace any line of code back to a spec requirement

Easier debugging: When bugs appear, I check against specs to find the gap

Consistent quality: Each piece of code follows the same specification process

Reduced technical debt: Design decisions are documented, not lost in code

Common Mistakes to Avoid

I’ve made these mistakes when implementing SDD:

Skipping the specification phase: Tempting to jump straight to code, especially for “simple” features. This defeats the purpose.

Writing vague specifications: “Add good error handling” is not a spec. “Return 400 for invalid email format with message ‘Invalid email’” is a spec.

Not verifying against specs: Writing specs but not using them for review wastes the effort.

Over-specifying: Don’t specify implementation details that don’t matter. Focus on behavior and contracts.

Treating AI output as final: Even with specs, review is necessary. AI might interpret specs differently than intended.

Frameworks That Support SDD

Two mature frameworks implement this approach:

OpenSpec: Generates structured specifications from natural language requirements. Creates proposals, designs, specs, and tasks in a standard format.

Superpowers: Takes a spec-first approach with built-in verification. Emphasizes incremental implementation with continuous validation.

Both frameworks recognize the same principle: the bottleneck in AI-assisted development is not code generation, but specification and verification.

When to Use SDD

SDD adds overhead, so it’s not always appropriate:

Use SDD when:
- Building new features with complex requirements
- Working on code that will be maintained long-term
- Multiple developers will interact with the code
- Requirements might change during development

Skip SDD when:
- Quick prototype or proof-of-concept
- One-off script or temporary solution
- Requirements are trivial and unambiguous
- Time pressure outweighs long-term quality

Summary

In this post, I showed how Spec-Driven Development solves the core problem of AI coding: ensuring generated code matches your intent. By writing specifications before implementation, you create a contract that constrains AI behavior and provides a verification baseline.

The key point is that as AI coding assistants improve, the challenge shifts from “how to write code” to “how to write correct code.” Specifications bridge this gap by making design decisions explicit and verifiable.

To implement SDD:

Generate proposal documents for new features
Create detailed design documents with architecture decisions
Write specifications with exact requirements and acceptance criteria
Break down into implementation tasks
Verify each implementation against its specification

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!