Spec-Driven Development with AI: Generate Specs from Code, Then Build Better

Introduction: The Spec-Driven Development Revolution

AI coding assistants like Cursor, Claude Code, Windsurf, and GitHub Copilot have transformed software development. But there’s a catch: they work best with clear specifications. The problem? Most existing codebases lack comprehensive specs, and writing them from scratch is overwhelming.

Enter a practical solution: generate specs from your existing code first, then use them to drive AI-powered development forward.

The Spec-Driven Development Challenge

Modern AI agentic coding tools excel when given clear context about:

• System architecture and component relationships
• Data models and flows
• API contracts and integration points
• Business capabilities and features
• Technical decisions and constraints

Without this context, AI assistants make assumptions, introduce inconsistencies, and struggle to understand impact. The solution isn’t to abandon spec-driven development—it’s to make creating specs effortless.

Start Here: Generate Specs from Your Existing Codebase

The fastest path to spec-driven development is starting with what you already have. gjalla analyzes your codebase and automatically generates:

• Architecture specifications: System components, containers, and their relationships
• Data model documentation: Entities, schemas, and data flows
• Capability catalogs: Features, APIs, and business functionality
• Decision records: Architectural choices and rationale
• Technology stack inventory: Dependencies and external integrations

How it works:

1. Connect gjalla to your repository
2. AI agents analyze your code structure, imports, and patterns
3. Comprehensive specs are generated automatically
4. Export in formats optimized for AI tools

This gives you a foundation of accurate, current specifications without manual documentation effort.

Choose Your AI Coding Tool

Once you have specs, you can leverage any spec-driven AI coding tool:

Popular AI Agentic Coding Tools

Cursor: IDE with built-in AI that uses project context and specs to generate code that fits your architecture. Excellent for feature development with .cursorrules files.

Claude Code: Terminal-based AI coding assistant that can read specs, understand system context, and make architectural changes. Strong at complex refactoring.

Windsurf: Agentic IDE with flow state features, uses specs to maintain consistency across multi-file changes. Great for large-scale modifications.

GitHub Copilot Workspace: GitHub-integrated environment that leverages specs for issue-to-PR workflows. Ideal for teams already on GitHub.

Aider: Command-line AI pair programmer that works with git and can reference spec files. Best for CLI-focused developers.

All of these tools benefit from clear specifications. The better your specs, the more accurate and contextual the AI-generated code.

The Spec-Driven Development Workflow

Here’s the practical workflow that combines spec generation with AI coding:

1. Generate Initial Specs (gjalla)

Start by extracting specs from your existing codebase:

• Architecture components and connections
• Data models and relationships
• Features and capabilities
• Tech stack and dependencies

2. Develop with Specs (Your AI Tool)

Use your preferred AI coding tool with the generated specs:

• Reference architecture specs when adding new services
• Check data model specs before schema changes
• Verify capability specs to avoid duplication
• Consult decision records for consistency

3. Manage Aggregate Specs (gjalla)

As your system evolves, keep specs synchronized:

• Consolidate: Merge specs from multiple features and workflows
• Detect changes: gjalla monitors codebase changes
• Track drift: Identify where implementation diverges from documented architecture

Key Benefits of This Approach

Start Without the Burden

Don’t write specs from scratch. Generate them from existing code and iterate forward.

AI Tools Work Better

Give your AI assistant the context it needs to make intelligent decisions that align with your architecture.

Single Source of Truth

Specs stay synchronized with your actual codebase, not maintained separately in wikis or docs.

Scale Across Teams

Multiple developers and AI agents work from the same specifications, maintaining consistency.

Bridge Legacy and New

Generate specs from legacy code, then use modern AI tools to evolve it systematically.

Real-World Scenarios

Scenario 1: Migrating to Microservices

• Generate specs from your monolith
• Use AI tools to refactor with clear service boundaries
• Track aggregate specs across new services

Scenario 2: Onboarding AI Coding Tools

• Team wants to adopt Cursor but lacks documentation
• Generate comprehensive specs with gjalla
• Configure Cursor rules based on generated architecture

Scenario 3: Multi-Service Coordination

• Ten microservices, inconsistent patterns
• Generate specs for each service
• Use aggregate view to standardize and AI tools to refactor

Scenario 4: Audit and Compliance

• Need architecture documentation for audit
• Generate current-state specs automatically
• Use AI tools to implement compliance requirements with spec validation

Beyond Individual Specs: The Aggregate View

As systems grow, managing specifications across multiple services, repositories, and teams becomes crucial. gjalla’s aggregate spec management provides:

• Cross-service visibility: See how components interact across repositories
• Consistency checking: Identify architectural drift and pattern violations
• Impact analysis: Understand ripple effects of changes
• Dependency mapping: Track internal and external dependencies holistically
• Knowledge graph: Navigate your entire architecture as interconnected specs

This is where spec-driven development scales from individual features to enterprise architecture.

Getting Started

Step 1: Connect gjalla to your repository Step 2: Review and refine generated specs Step 3: Export specs in your AI tool’s preferred format Step 4: Configure your AI coding tool to reference specs Step 5: Develop features with AI assistance Step 6: Let gjalla track changes and update specs automatically

Conclusion

Spec-driven development with AI doesn’t require months of upfront documentation. Start by generating specs from your existing code with gjalla, use any AI coding tool you prefer with those specs, and maintain aggregate specifications as your system evolves.

The result? AI assistants that understand your architecture, code that stays consistent, and specifications that actually reflect reality—because they’re generated from it.

Stop fighting to keep specs and code synchronized. Generate specs from code, build with AI, and let automation manage the rest.