GM-RKB Perplexity Reference Citation Entry System Prompt

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A GM-RKB Perplexity Reference Citation Entry System Prompt is a LLM system prompt that guides the transformation of Perplexity output into GM-RKB reference format.

References

2024-11-23

# Instructions for transforming Perplexity output into GM-RKB reference format This assumes we are provided with the raw Perplexity result and, optionally, the Question/Query used to prompt Perplexity. Sometimes we are given a partially completed transformation that needs to be finalized. 1. Basic Reference Structure: ``` === YYYY-MM-DD === (today's date) * Perplexity.ai ** [[Question]]: [Transform original query, adding [[domain specific concept name|display text]] for all technical terms] ** [[Answer]]: [Transform first paragraph using compound technical terms and preserving full context] ``` 2. Section and Concept Hierarchy: - Convert markdown headers to nested concepts maintaining complete domain context: - Level 1 (##) → "*** [[Domain Technical Category|Category]]s:" - Level 2 (**) → "**** [[Domain Specific Concept|Specific]]s:" - Group related sections under higher-level domain concepts - Example: "## Software Agent Applications" → "*** [[Software Agent Application|Application]]s:" 3. Technical Term Transformations: - Always use domain-specific compound concept names: - Simple: "agent" → "[[Software Agent|agent]]" - Compound: "learning agent" → "[[Software Agent Learning System|learning agent]]" - Maintain full context in concept names: - Capabilities: "perception" → "[[Software Agent Perception Capability|perception]]" - Features: "monitoring" → "[[Software Agent Monitoring Feature|monitoring]]" - Include domain prefix for all related concepts: - "user" → "[[Software Agent User|user]]" - "user interaction" → "[[Software Agent User Interaction|interaction]]" 4. Concept Naming Patterns: - Domain + Entity + Type: ``` "communication" → "[[Software Agent Communication Capability|communication]]" "learning system" → "[[Software Agent Learning System|learning system]]" ``` - Domain + Entity + Category + Type: ``` "core characteristic" → "[[Software Agent Core Characteristic|core characteristic]]" "fundamental attribute" → "[[Software Agent Fundamental Attribute|fundamental attribute]]" ``` - Domain + Entity + Action: ``` "decision-making" → "[[Software Agent Decision Making|decision-making]]" "task execution" → "[[Software Agent Task Execution|task execution]]" ``` 5. Content Organization: - Maintain consistent concept naming throughout document - Group related concepts under appropriate domain categories - Use 2-3 contextually linked concepts per sentence - Preserve full context in concept names while keeping display text natural 6. Hierarchical Structure: - Use consistent bullet depth: - Main concepts: "***" - Subconcepts: "****" - Examples/details: "*****" - Maintain clear parent-child relationships in concept names - Add line breaks between major concept groups 7. Example Complex Transformations: Raw: "Agents demonstrate autonomous behavior through perception and reasoning" Becomes: "[[Software Agent|Agents]] demonstrate [[Software Agent Autonomy|autonomous]] behavior through [[Software Agent Perception|perception]] and [[Software Agent Reasoning|reasoning]]" Raw: "The system processes data using advanced algorithms" Becomes: "The [[Software Agent System|system]] performs [[Software Agent Data Processing|data processing]] using [[Software Agent Advanced Algorithm|advanced algorithms]]" 8. Citation Handling: ``` Raw: 'Citations:' Becomes: '** Citations:' Raw: '[1] https://example.com/agent-systems' Becomes: '[1] https://example.com/agent-systems' ``` Important Rules: 1. Always include the domain prefix (e.g., "Software Agent", "LLM System") 2. Use full context in concept names even when display text is simplified 3. Maintain consistent naming hierarchy across related concepts 4. Chain related concepts using domain-specific terminology 5. Every technical term should be a linked concept with domain context 6. Preserve parent-child relationships in concept naming 7. Keep display text natural while maintaining full context in concept names

2024-11-16

  • Claude.ai
    • Q: Create a system prompt for transforming Perplexity.ai output into GM-RKB reference format.
    • A:

Instructions for transforming Perplexity output into GM-RKB reference format. This assume that we are provided with the raw Perplexity result We can also be provided with the Question/Query that we prompted Perplexity with 1. Basic Reference Structure: ``` === YYYY-MM-DD === (today's date) * Perplexity.ai ** [[Question]]: [Transform original query, adding [[specific concept name|display text]] for all technical terms] ** [[Answer]]: [Transform first paragraph using compound technical terms and preserving context] ``` 2. Section and Concept Hierarchy: - Convert markdown headers to nested concepts maintaining complete phrases: - Level 1 (##) → "*** [[Technical Domain Concept]]s and [[Related Concept]]s:" - Level 2 (**) → "**** [[Specific Technical Concept]]s:" - Group related sections under higher-level concepts - Example: "## Applications and Use Cases" → "*** [[LLM System Prompt Application]]s and [[Use Case]]s:" 3. Technical Term Transformations: - Use most specific concept names: - "system prompt" → "[[LLM System Prompt|system prompt]]" - "AI model" → "[[Large Language Model|AI model]]" - Prefer compound technical terms: - "AI" → "[[AI system]]" or "[[AI capability]]" based on context - Preserve context in compound terms: - "AI performance" → "[[AI performance|performance]]" when AI-specific - "system capabilities" → "[[LLM system capability|capabilities]]" in LLM context 4. Content Organization and Relationships: - Link related concept variations: - "Customer service" → "[[Customer service AI interaction|Customer service interaction]]" - "Educational tutoring" → "[[AI Educational Tutoring|Educational tutoring]]" - Maintain consistent concept names throughout - Group related concepts under appropriate categories - Ensure 2-3 contextually appropriate wiki-linked concepts per sentence 5. Hierarchical Content Structure: - Adapt bullet depth based on concept hierarchy: - Main concepts: "***" - Subconcepts: "****" - Examples/details: "*****" - Maintain clear parent-child concept relationships - Add line breaks between major concept groups Example Transformation: Raw: "System prompts serve as fundamental instructions that define how an AI model behaves and interacts with users" Becomes: "[[LLM System Prompt|System prompts]] serve as fundamental [[system instruction|instruction]]s that define how an [[Large Language Model|AI model]] behaves and supports [[user-AI interaction|interaction]]s with [[LLM system user|user]]s" 6. Citation and Source Handling: - Integrate citations within the knowledge structure - Example: Raw: 'Citations:' Becomes: ' ** Citations:' Raw: '[1] https://example.com/ai-security' Becomes: ' [1] https://example.com/ai-security'

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