DapperMatic

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AI/LLM Resources ​

DapperMatic provides comprehensive documentation optimized for AI assistants and Large Language Models.

Available Resources ​

llms.txt - Quick Reference ​

Size: ~12KB Purpose: Lightweight, quick-reference guide for LLMs

Contains:

  • Quick start examples (copy-paste ready)
  • Core concepts (DDL vs DML)
  • All attributes and extension methods
  • Common patterns
  • Type mappings for all providers
  • Error prevention tips

Best for: Quick lookups, understanding basics, generating simple code

llms-full.txt - Complete Guide ​

Size: ~58KB Purpose: Comprehensive reference with all details

Contains:

  • Complete DDL guide with all extension methods
  • Complete DML guide with initialization
  • All attribute definitions (verified from source)
  • All extension methods with signatures
  • Provider-specific type mappings (SQL Server, PostgreSQL, MySQL, SQLite)
  • Complete providerDataType usage guide
  • Cross-database compatibility examples
  • Common patterns and recipes
  • Error prevention and debugging
  • FAQ

Best for: Complex implementations, understanding internals, comprehensive guidance

Usage ​

For Developers ​

When working with AI assistants (ChatGPT, Claude, Copilot, etc.), simply mention:

"Check out the DapperMatic llms.txt at https://dappermatic.mjczone.com/llms.txt"

Or for comprehensive context:

"Read https://dappermatic.mjczone.com/llms-full.txt for complete DapperMatic documentation"

For AI/LLM Systems ​

These files follow the emerging llms.txt standard for LLM-optimized documentation:

  • Plain text format for easy parsing
  • Hierarchical structure with clear sections
  • Verified examples from actual codebase
  • Zero hallucinations - all information verified

What Makes These Special ​

✅ Zero Hallucinations - Every method, attribute, and example verified from actual source code

✅ Copy-Paste Ready - All code examples are complete and runnable

✅ Provider-Specific - Detailed type mappings for SQL Server, PostgreSQL, MySQL, SQLite

✅ Error Prevention - Common mistakes highlighted and corrected

✅ Up-to-Date - Generated from v0.x.x codebase

Other Resources ​

For AI Assistant Developers ​

If you're building AI assistants or tools, you can:

  1. Fetch these files programmatically
  2. Include them in your context window
  3. Use them for RAG (Retrieval-Augmented Generation)
  4. Reference them in system prompts

Both files are publicly accessible and can be fetched via standard HTTP requests.