Spec-Driven Development for AI Coding: The Complete Guide

TL;DR: Spec-Driven Development (SDD) means generating authoritative documentation before code. Not sure if you're ready? Run the Spec Readiness Score.

Table of Contents

1. What is Spec-Driven Development?
2. SDD vs Vibe Coding
3. The Core Principle
4. Functional vs Technical Specs
5. The SDD Artifact Stack
6. SDD Workflow for AI Tools
7. Context Ark + Cursor Workflow
8. Context Ark + Cline Workflow
9. Common Mistakes
10. Free Templates

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What is Spec-Driven Development?

Spec-Driven Development (SDD) is a methodology where you generate comprehensive specifications before writing any code. These specs become the "source of truth" that AI coding tools reference.

The SDD Equation

Complete Specs + AI Context = Reliable Code

Instead of prompting AI with vague requests and hoping for the best, you:

1. Generate a complete spec pack (PRD, API spec, schema, architecture)
2. Feed that pack to your AI tool as context
3. Prompt against the specs ("implement the /api/users endpoint from the API spec")
4. Validate output against the same specs

Why Now?

AI coding tools (Cursor, Cline, v0, Lovable, Windsurf) are powerful but context-starved. They hallucinate because they don't know your app's actual structure. SDD solves this by giving them authoritative documentation.

TL;DR for this section: SDD = specs before code. AI tools reference specs instead of guessing.

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SDD vs Vibe Coding

Vibe coding meaning is the default mode: prompt AI with loose descriptions, iterate on output, fix hallucinations as you go.

Aspect	Vibe Coding	Spec-Driven Development
Starting point	Idea in your head	Documented spec pack
AI context	None or minimal	Full project specs
Hallucination rate	High (30-60% of outputs)	Low (<10%)
Rework cycles	Many (3-10 per feature)	Few (1-2 per feature)
Scalability	Breaks around 10K LOC	Scales to 100K+ LOC
Team handoff	Nightmare	Smooth
Documentation	Absent or outdated	Always current

When Vibe Coding Works

• Prototypes (throwaway code)
• Solo projects under 1,000 LOC
• Exploration / learning

When SDD is Required

• Production apps
• Team projects (any size)
• Anything you'll maintain > 30 days
• Code that handles money, auth, or user data
• Essential for AI for product managers and teams.

TL;DR for this section: Vibe coding is fast but fragile. SDD is reliable and scalable.

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The Core Principle

The SDD core principle:

"AI tools can only be as good as the context you give them."

If you give them nothing, they invent. If you give them specs, they implement.

The Context Hierarchy

1. Global rules (coding standards, patterns)
2. Project context (architecture, tech stack)
3. Feature specs (PRD, acceptance criteria)
4. Implementation specs (API, schema, components)
5. Task context (specific file, specific function)

SDD ensures layers 1-4 exist before you ask for layer 5.

TL;DR for this section: AI tools implement what you document. No docs = inventions. Specs = reliability.

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Functional vs Technical Specs

Functional specs define the 'what', while technical specs define the 'how'.

Functional Specs (The "What")

Define what the system should do from the user's perspective:

• PRD (Product Requirements Document): Features, user stories, acceptance criteria
• UX Flows: Wireframes, user journeys, interaction patterns
• Business Rules: Validation logic, calculations, permissions

Technical Specs (The "How")

Define how the system implements the functional requirements:

• API Spec: Endpoints, payloads, authentication
• Database Schema: Tables, columns, relationships
• Architecture Doc: Components, integrations, data flow
• Component Inventory: UI components, props, states

The Separation Problem

Martin Fowler noted that separating functional and technical specs is one of the "hard problems" in SDD tools. Many tools conflate them, leading to specs that are neither good requirements nor good designs.

Context Ark's Approach

Context Ark generates both layers with clear separation:

Layer	Artifacts
Functional	PRD, Scope Lock, User Stories, Acceptance Criteria
Technical	API Spec, Database Schema, Architecture Doc, Component Inventory

The functional specs are stable (change rarely). The technical specs evolve (change with implementation decisions).

TL;DR for this section: Functional = what users see. Technical = how it's built. Keep them separate.

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The SDD Artifact Stack

The complete stack of artifacts needed to prevent AI hallucinations.

A complete SDD implementation requires these artifacts:

Tier 1: Foundation (Required)

Artifact	Purpose	Prevents
PRD document	Scope lock (use PRD generator)	Scope creep, invented features
API Spec	Endpoint contracts (OpenAPI)	Wrong endpoints, bad payloads
Database Schema	Tables, columns, relationships	Wrong columns, missing relations

Tier 2: Architecture (Required for teams)

Artifact	Purpose	Prevents
Architecture Doc	System components, data flow	Pattern mismatch, wrong integrations
Tech Stack Doc	Frameworks, versions, dependencies	Wrong tech, version conflicts
Component Inventory	UI components, props, states	Inconsistent UI, duplicate components

Tier 3: Operations (Required for production)

Artifact	Purpose	Prevents
Deployment Playbook	How to ship, environments	Deploy failures, env mismatches
Security Spec	Auth, permissions, data handling	Security holes, compliance issues
Observability Spec	Logging, monitoring, alerting	Silent failures, blind debugging

Context Ark's 60 Docs

Context Ark generates 60 artifacts across all three tiers from a single brain dump. This ensures no gaps in your spec coverage.

TL;DR for this section: Full SDD needs 9-15 core artifacts. Gaps in coverage = specific hallucination types.

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SDD Workflow for AI Tools

The general SDD workflow for any AI coding tool:

Phase 1: Generate Specs

Brain Dump → Intake Questions → Kernel Build → Doc Generation

Output: Complete spec pack (PRD, API, Schema, Architecture, etc.)

Phase 2: Configure Tool

1. Add spec files to the tool's context path

Organize your artifacts clearly so AI tools can find them.

2. Configure rules/instructions referencing specs
3. Set up file references (@file syntax)

Phase 3: Prompt Against Specs

Bad:  "Create a user signup flow"
Good: "Implement the POST /api/auth/signup endpoint from the API spec in @api-spec.yaml"

Phase 4: Validate Output

1. Check API calls match spec
2. Check database queries use real columns
3. Check imports exist
4. Check scope maintained

Phase 5: Update Specs

When implementation reveals needed changes:

1. Update the spec first
2. Then update the code
3. Specs remain source of truth

TL;DR for this section: Generate → Configure → Prompt → Validate → Update. Specs stay current.

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Context Ark + Cursor Workflow

Setup

1. Generate spec pack from Context Ark
2. Export to your repo's root
3. Add to .cursor/rules/:

## Project Context

This project uses Spec-Driven Development. All implementations must:

1. Reference the specs in /docs/ before implementing
2. Follow the patterns in architecture.md
3. Use only endpoints defined in api-spec.yaml
4. Query only columns defined in schema.sql

When implementing any feature, first check:

- @docs/prd.md for scope and acceptance criteria
- @docs/api-spec.yaml for endpoint contracts
- @docs/schema.sql for database structure

Prompting

Use @file references:

Using @docs/api-spec.yaml, implement the GET /api/projects/{id} endpoint.
Include error handling per the error spec.

Validation

After each generation:

Check this implementation against @docs/api-spec.yaml:
- Are the response shapes correct?
- Are all error codes handled?
- Is auth implemented per spec?

→ Full Cursor Workflow Guide

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Context Ark + Cline Workflow

Setup

1. Generate spec pack from Context Ark
2. Create .clinerules:

## Cline Project Rules

## Context Loading

Before any implementation, load and reference:

- /docs/prd.md
- /docs/api-spec.yaml
- /docs/schema.sql
- /docs/architecture.md

## Implementation Rules

1. Only implement features in the PRD
2. Only use endpoints in the API spec
3. Only query columns in the schema
4. Follow patterns in architecture doc

## When Uncertain

Ask for clarification rather than inventing.
Reference the spec section that's unclear.

Prompting

Be explicit about spec references:

Implement the user signup flow.
Reference: PRD Section 3.2 (User Onboarding)
Endpoint: POST /api/auth/signup from api-spec.yaml
Schema: users table from schema.sql

→ Full Cline Workflow Guide

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Common Mistakes

1. Specs Too Vague

❌ "The app should be fast" ✅ "API responses < 200ms p95, page load < 2s"

2. Specs Not Available to AI

❌ Specs in Notion, code in repo ✅ Specs in repo, referenced in prompts

3. Specs Outdated

❌ Wrote specs, never updated ✅ Update specs when implementation changes

4. Skipping Validation

❌ AI generated → shipped ✅ AI generated → validated against spec → shipped

5. Mixing Functional/Technical

❌ PRD includes database column names ✅ PRD describes features; schema describes columns

6. No Non-Goals

❌ "Build a project management tool" ✅ "Build project management. NON-GOAL: real-time collaboration"

TL;DR for this section: Common fails: vague specs, inaccessible specs, outdated specs, skipped validation, no non-goals.

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Free Templates

Start your SDD journey with these templates:

Template Packs

• Spec-Driven Development Template Pack – Full SDD starter kit
• PRD Template for AI Apps – Scope lock + user stories
• API Spec Template (OpenAPI) – Endpoint contracts

Workflow Guides

• Context Ark + Cursor Workflow
• Context Ark + Cline Workflow
• Context Ark + v0 Workflow

Tools

• Spec Readiness Score – Check your spec coverage

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Next Steps

1. Download the template pack: SDD Templates
2. Generate specs for your next project: Use Context Ark or the templates
3. Configure your AI tool: Add specs to context
4. Start prompting against specs: Reference specific sections
5. Validate everything: Never ship without checking

Ready to stop vibe coding? Generate your spec pack →

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Last updated: January 2026