AISpec

A specification language for AI-first development that shifts focus from implementation to intent through structured solution space reduction.

Built on the What-Boundaries-Success (WBS) Framework, AISpec provides a practical implementation of systematic constraint engineering for AI systems.

Overview

AISpec is a declarative format for defining software features in a way that's both human-readable and AI-parseable. It focuses on three key aspects:

1. Declaring WHAT we want
2. Setting BOUNDARIES
3. Defining SUCCESS CRITERIA

Core Format

Feature: Name {
  What:
    - "Clear action items"
    - "Each one executable"
    
  Boundaries:
    - "Performance limits"
    - "Resource constraints"
    - "Business rules"
    
  Success:
    - "Measurable outcomes"
    - "Clear metrics"
    - "Expected behavior"
}

Example

Feature: UserAuth {
  What:
    - "Handle user login"
    - "Issue JWT token"
    - "Track attempts"

  Boundaries:
    - "Max 5 attempts/hour"
    - "Token expires 24h"
    - "Passwords hashed"

  Success:
    - "Valid users login"
    - "Invalid blocked"
    - "Response < 200ms"
}

Extended Format

For more complex features, the format can be extended with additional sections:

Feature: Name {
  # Core sections as above, plus:
  
  Technical:
    framework: "specified-framework"
    database: "database-type"
    patterns: ["design-patterns"]
    
  Dependencies:
    required: ["necessary-packages"]
    optional: ["nice-to-have-packages"]
    
  Security:
    auth: "requirements"
    encryption: "needs"
    rate_limits: "constraints"
}

Examples

See the examples/ directory for:

• Basic usage patterns
• Advanced features including Flow specifications
• Real-world implementation scenarios

Theory

AISpec's effectiveness comes from its ability to reduce the solution space through structured constraints. For example:

Traditional prompt:

"Write authentication code"
# Solution space: 1000s of possible implementations

AISpec format:

Feature: Auth {
  What: [specific criteria]
  Boundaries: [clear constraints]
  Success: [measurable outcomes]
}
# Solution space: Reduced to few viable implementations

This structured reduction leads to more accurate and consistent AI-generated code.

Philosophy

Instead of manually verifying AI-generated code, AISpec provides a higher abstraction layer that focuses on intent and outcomes rather than implementation details. This allows:

• Clear communication between humans and AI
• Focus on business requirements
• Measurable success criteria
• Consistent implementation patterns

Origin

AISpec, built on the What-Boundaries-Success (WBS) Framework, was created by Chris Bora (@chrisbora_) and first proposed on December 24, 2024 in this tweet.

Community

Join our community on Discord to share ideas, provide feedback, and collaborate on real-world AISpec applications: Discord Link

Learn More

• WBS Framework: WBS-Framework.md
• Follow development: @chrisbora_

License

MIT License - see LICENSE file for details