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Add LLM-friendly documentation#7

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askdevai-bot:add-llm-friendly-documentation
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Add LLM-friendly documentation#7
askdevai-bot wants to merge 1 commit into
zenbase-ai:mainfrom
askdevai-bot:add-llm-friendly-documentation

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Summary

This PR adds comprehensive LLM-friendly documentation (LLM.md) to the LLML repository. The documentation provides a complete API reference optimized for AI/LLM consumption and developer productivity.

What's Added

  • Complete API Reference: Covers all four language implementations (Python, TypeScript/JavaScript, Rust, Go)
  • Clear Examples: Practical usage patterns and code samples for each language
  • Installation Instructions: Step-by-step setup for all supported languages
  • Function Signatures: Detailed parameter descriptions and return types
  • LLM-Optimized Format: Structured for easy parsing and understanding by AI systems

Key Features

  • Multi-language consistency maintained across all implementations
  • Clear section organization for easy navigation
  • Comprehensive coverage of the llml() function and formatters
  • Installation and usage examples for each language ecosystem
  • Optimized for both human developers and AI/LLM consumption

Testing

  • Verified documentation accuracy against existing codebase
  • Ensured consistency with contribution guidelines
  • Follows the project's multi-language approach

Contribution Guidelines Compliance

Documentation Updated: Added comprehensive API documentation
Multi-Language Consistency: Covers all four language implementations
Clear Examples: Includes practical usage patterns
No Breaking Changes: This is purely additive documentation

This documentation will help developers quickly understand and integrate LLML into their projects while providing AI systems with structured, comprehensive API information.


A developer on Askdev.AI requested this update

This commit adds comprehensive LLM-friendly documentation that provides:
- Complete API reference for all four language implementations (Python, TypeScript, Rust, Go)
- Clear examples and usage patterns
- Installation instructions for each language
- Detailed function signatures and parameters
- Optimized format for AI/LLM consumption

The documentation follows the project's multi-language approach and maintains consistency across all implementations.
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