GM-RKB Perplexity Reference Citation Entry System Prompt
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.
- Context:
- It can specify GM-RKB Reference Structure Rules.
- It can define Content Transformation Rules.
- It can provide Example Transformations.
- It can specify Basic Reference Structure (for date format, source line, question section, answer section).
- It can define Section Transformation Rules (for header conversion, concept hierarchy, section grouping).
- It can include Technical Term Transformation Rules (for concept naming, compound term handling, context preservation).
- It can provide Content Organization Rules (for concept relationships, term consistency, concept grouping).
- It can establish Hierarchical Structure Rules (for bullet depth, parent-child relationships).
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- Example(s):
- the
2024-11-16one below. - ...
- the
- Counter-Example(s):
- Citation Management Prompts, which only handle reference formatting.
- Wiki Syntax Converters, which only focus on basic wiki markup.
- See: System Prompt Design, Reference Formatting, Content Transformation, GM-RKB Style Guide.
References
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'