Automatic Prompt Optimization Framework
This project contains the original white paper for Language Construct Modeling (LCM) v1.13, authored by Vincent Shing Hin Chong. It introduces a novel framework for prompt-layered semantic control in large language models (LLMs), built upon the Meta Prompt Layering (MPL) structure. LCM formalizes a modular system of prompt orchestration, enabling
Semantic Logic System v1.0 — A system that use language to construct and model LLMs.
A meta-prompting system that transforms raw prompts into production-ready, XML-structured prompts optimized for Claude Opus 4.6. 10 codified rules, 10-component framework, complexity-based routing — based on Anthropic's official best practices.
THE META PROMPT 是一个专业化的提示词仓库,提供经过实战验证的结构化元提示词模板,旨在将通用大语言模型转化为具有增强可靠性和精确度的领域专用推理智能体。与传统提示词库不同,本项目专注于元级别的指令框架,支持系统化任务分解、基于角色的推理以及自我改进的智能体架构。
🛠️ Optimize any raw prompt into a best-practice, production-ready prompt for Claude Opus 4.6 in seconds, enhancing clarity and effectiveness.
LLM code generation benchmark — Claude vs Gemini vs DeepSeek vs Grok on a physics simulation task. Compares prompting strategies and artifact modes.
🚀 Optimize your prompts for AI systems easily and boost performance with intuitive tools and features designed for better results.
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