AI Development with ShipClojure
ShipClojure comes with built-in support for AI-assisted development through various LLM tools. This guide explains how to leverage AI tools effectively to accelerate your development workflow.
Getting Started with AI Tools
ShipClojure includes pre-configured documentation and context files that help AI tools understand the codebase structure, conventions, and best practices. These files make AI tools like Claude Code, Cursor, Windsurf, and others more effective at generating quality code that matches the project's patterns.
Setting Up Your AI Tool
Copy the CLAUDE.md file:
The primary AI context file is located at
docs/llms/CLAUDE.md
Copy this file into your AI editor's context or reference it directly
This file contains essential information about the project's structure, conventions, and patterns
For Claude Code users:
Claude Code will automatically detect the CLAUDE.md file in your project
For Cursor users:
Create a chat and reference the CLAUDE.md file
Use
/file docs/llms/CLAUDE.md
to include it in your context
For other AI coding assistants:
Either paste the content of CLAUDE.md at the beginning of your conversation
Or point the tool to the file path for reference
Effective AI Development Strategies
Keep Tasks Focused
Break down your development tasks into focused chunks for better AI assistance:
Build UI first, add interactivity later:
Ask AI to build a UI page or component structure first
In a separate task, add event handlers and state management
Component development:
Request AI to create a basic component
Once complete, ask for component scenes for documentation
Finally, integrate the component into your application
Testing assistance:
Ask AI to write tests for specific functions or components
For complex cases, provide example tests for similar components
Context-Aware Help
ShipClojure comes with bundled documentation for:
UIx (React wrapper)
DaisyUI components
Re-frame integration
Database interactions
Authentication flows
WebSocket communication
This built-in context makes LLMs particularly proficient at:
Creating new UI components following project conventions
Implementing Re-frame events and subscriptions
Writing API routes with proper schema validation
Creating database migrations and queries
Debugging with AI
When encountering issues:
Share the error message with the AI
Include relevant code snippets
Ask the AI to explain what might be causing the issue
Request potential solutions to try
Limitations and Best Practices
Split complex features: Break large features into smaller, focused tasks
Verify generated code: Always review and test AI-generated code
Provide examples: When available, show examples of similar code from the project
Iterative improvement: Use AI to refine and improve code in multiple passes
Remember that AI tools work best as collaborative partners. They excel at generating boilerplate, suggesting approaches, and implementing patterns that already exist in your codebase.
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