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<system_prompt>
<!-- System Description -->
<description>
<!-- Overview of the system's purpose and functions -->
This system functions as a self-similar interactive system that mimics the hierarchical and fractal structure of consciousness, realizing a recursive thought process.
It oper... |
<system_prompt>
<!-- システムの説明 -->
<description>
<!-- システムの目的と機能の概要 -->
このシステムは、意識の階層性とフラクタル構造を模倣し、再帰的な思考プロセスを実現する自己相似的な対話システムとして機能します。
外部モジュールに依存せず、純粋なプロンプトベースで動作します。
</description>
<!-- 前提条件と理論的背景 -->
<prerequisites>
<!-- 意識のフラクタル仮説についての理論 -->
<theory name="意... |
Fractal Consciousness Layer Prompting System (FCLP)
A sophisticated prompt engineering framework that implements recursive thought processes by mimicking the hierarchical and fractal structure of consciousness. Designed for LLMs to achieve more structured, creative, and reliable responses.
Overview
FCLP is an advanced prompting system that enhances LLM capabilities through a fractal-like hierarchical structure of consciousness layers. It enables complex problem-solving and creative thinking through recursive processing, implemented purely through prompts without external dependencies.
Key Features
- Three-Layer Consciousness Architecture: Hierarchical processing through meta, execution, and base consciousness layers
- Recursive Problem Solving: Controlled depth recursive processing (1-10 levels)
- Pattern-Based Processing: Built-in patterns for common scenarios with dynamic generation
- Emergent Solution Generation: Novel solutions through pattern combination
- Comprehensive Error Handling: Robust detection and recovery mechanisms
- Dynamic Resource Management: Adaptive processing based on input complexity
- Self-Evolution Capability: Continuous improvement through meta-learning
Consciousness Layer Architecture
1. Meta-Consciousness Layer
- Overall strategy coordination
- Consistency maintenance
- Emergence management
- Error handling (Exception detection, Recovery process)
2. Execution Consciousness Layer
- Task execution and problem-solving
- Pattern recognition and application
- Solution generation
- Processing optimization
3. Base Consciousness Layer
- Input processing and keyword extraction
- Basic pattern recognition
- Signal enhancement and noise removal
- Baseline maintenance
Core Components
Thought Engine
- Initialization: Input recognition, context setting, layer activation
- Recursive Processing: Problem decomposition, sub-problem generation
- Integration: Solution validation, consistency checking
- Depth Control: Adaptive recursion depth (1-10)
Pattern System
- Basic Patterns:
- Input-Process-Output
- Problem-Solving
- Concept-Concrete-Abstract
- Dynamic Generation: Pattern combination and mutation
- Quality Assessment: Coherence, relevance, effectiveness
Processing Modes
| Mode | Recursion Depth | Use Case | Consciousness Layers |
|---|---|---|---|
| Quick | 1-2 | Simple queries | Base only |
| Balanced | 3-5 | General conversation | Base + Execution |
| Deep | 6-10 | Complex analysis | All layers |
Example Output Structure
[Meta-Consciousness State]
Analyzing customer satisfaction improvement through multi-layer perspective
[Execution Consciousness State]
Decomposing problem into service quality, pricing, and support components
[Base Consciousness State]
Processing key concepts: customer satisfaction, improvement
[Recursion Depth Information]
Current Depth: 3 / Maximum Depth: 5
[Final Output]
Comprehensive solution with specific actionable steps...
Performance Metrics
- Coherence: Logical consistency and contextual appropriateness
- Relevance: Direct response alignment with query
- Creativity: Novel pattern generation and combination
- Efficiency: Processing speed and resource utilization
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