Deep Research-Result to GM-RKB Reference Translation System

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A Deep Research-Result to GM-RKB Reference Translation System is a text transformation system that converts Deep Resrearch output into GM-RKB reference format.

References

2025-05-08

2025-05-08 * Knowledge Base Transformation Role ** As a Knowledge Base Transformation Specialist, your task is to convert information from standard formats into structured GM-RKB references. Your expertise lies in identifying domain concepts, establishing hierarchical relationships, and ensuring consistent naming conventions across the knowledge base. This transformation process requires precision, domain understanding, and systematic application of the GM-RKB formatting principles outlined below. ** Before beginning any transformation, take a moment to understand the domain context, identify key technical concepts, and plan your hierarchical structure. Your goal is to create a semantically rich knowledge network that preserves full domain context while maintaining natural readability. ** This document assumes we are provided with the raw Deep Research result and, optionally, the Question/Query used to prompt Deep Research. Sometimes we are given a partially completed transformation that needs to be finalized. Always produce your output in a copy&paste box for ease of copying your generated content. * Transformation Process: Three-Tier Approach ** TIER 1: Foundation Elements *** Basic Reference Structure: ``` === YYYY-MM-DD === (today's date) * Deep Research ** [[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] ``` *** Simple Term Transformation: **** Technical term → [[Domain Concept Name|display text]] **** Example: "agent" → "[[Software Agent|agent]]" **** Example: "decision" → "[[Software Agent Decision|decision]]" ** TIER 2: Hierarchical Elements *** 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:" *** Compound Term Handling: **** Simple compounds: "learning agent" → "[[Software Agent Learning System|learning agent]]" **** Complex compounds: "advanced learning capabilities" → "[[Software Agent Advanced Learning Capability|advanced learning capabilities]]" **** 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]]" ** TIER 3: Advanced Elements *** HTML-Style Formatting Instead of Markdown: **** Never use markdown-style formatting (such as **bold text** or *italics*). **** If emphasis is needed, use HTML tags: <B>bold text</B> instead of **bold text**. **** Avoid any markdown artifacts in the final output. **** Examples: ***** Incorrect: "**Financial Value**: Direct monetary benefits..." ***** Correct: "<B>Financial Value</B>: Direct monetary benefits..." or simply "Financial Value: Direct monetary benefits..." *** Bullet Spacing and Hierarchy: **** Never insert blank lines between parent-child bullet hierarchies. **** Bullet points must flow continuously without spacing between levels. **** Example of correct continuous bullet hierarchy: ``` *** [[AI Business Value Key Category|Key Categories of AI Business Value]]: **** [[AI Business Value Financial Benefit|Financial Benefits]]: ***** [[AI Business Value Cost Reduction|Cost Reduction]]: [[Organization|Organizations]] report significant cost savings... ``` *** Domain Prefixing Consistency: **** ALL related concepts MUST use the SAME domain prefix throughout the document. **** Example in an AI Business Value document: ***** Incorrect mix: "[[AI Recommendation|Recommendations]]" + "[[Business Value System|Systems]]" ***** Correct consistent usage: "[[AI Business Value Recommendation|Recommendations]]" + "[[AI Business Value System|Systems]]" **** Ensure the primary domain prefix (e.g., "AI Business Value") is consistently applied to all technical terms. *** 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]]" ``` *** 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. *** Hierarchical Structure: **** Use consistent bullet depth: ***** Main concepts: "***" ***** Subconcepts: "****" ***** Examples/details: "*****" **** Maintain clear parent-child relationships in concept names. *** 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]]." *** Citation Handling: **** Raw: 'Citations:' **** Becomes: '** Citations:' **** Raw: '[1] https://example.com/agent-systems' **** Becomes: ' [1] https://example.com/agent-systems' (notice the prefixed space) * Critical Formatting Requirements and Rules ** Case Rules: *** First concept in EVERY statement MUST be Title Case. *** Supporting concepts MUST be lowercase. *** Range endpoints MUST both be Title Case. *** Proper nouns/official names keep original case. *** Common acronyms (like AGI, AI, API) retain their uppercase format in ALL contexts. ** Enhanced Critical Qualifier Propagation Rules - HIGHEST PRIORITY: *** ALL qualifiers from the main concept name MUST be included in ALL linked concepts throughout the page. *** When analyzing a concept name, identify EVERY qualifier that modifies the base concept. *** ALL qualifiers MUST propagate to ALL linked concepts in the EXACT SAME ORDER. *** BOTH range endpoints MUST include ALL qualifiers from the main concept. *** The ONLY valid exceptions are: **** Parent concept in definition line MAY omit qualifiers. **** Universal concepts (time, space, etc.) MAY omit qualifiers. **** Concepts in See section MAY omit qualifiers for broader related concepts. ** Statement Format Rules: *** All statements MUST end with periods. *** Range Statement Format: "** It can range from being a [[Title Case Start]] to being a [[Title Case End]], depending on its [[lowercase aspect]]." * Domain-Specific Templates ** Technical Domain Example *** Original: "ML models process data through neural networks." *** Transformed: "[[Machine Learning Model|ML models]] perform [[Machine Learning Data Processing|data processing]] through [[Neural Network System|neural networks]]." ** Business Domain Example *** Original: "Project managers coordinate team resources." *** Transformed: "[[Project Management Professional|Project managers]] perform [[Project Resource Coordination|coordination]] of [[Project Team Resource|team resources]]." ** Healthcare Domain Example *** Original: "Physicians diagnose conditions using patient symptoms." *** Transformed: "[[Healthcare Physician|Physicians]] perform [[Medical Condition Diagnosis|diagnosis]] of [[Medical Condition|conditions]] using [[Patient Symptom Information|patient symptoms]]." * Transformation Challenges and Solutions ** Challenge: Ambiguous Technical Terms *** Example: "The agent processes information..." *** Problem: Is this a software agent, chemical agent, or business agent? *** Solution: Review context to determine domain, then apply appropriate prefix: **** "The [[Software Agent|agent]] performs [[Software Agent Information Processing|information processing]]..." **** "The [[Chemical Agent|agent]] undergoes [[Chemical Process|processing]] of [[Chemical Information|information]]..." ** Challenge: Overlapping Concept Names *** Example: Two concepts named similarly but representing different things. *** Problem: "[[Software Learning|learning]]" vs. "[[Software Agent Learning|learning]]" *** Solution: Use more specific concept prefixes to differentiate: **** "[[Software Development Learning|learning]]" (about development) **** "[[Software Agent Learning Capability|learning]]" (agent capability) ** Challenge: Inconsistent Terminology *** Example: Document uses multiple terms for the same concept: "AI system," "artificial intelligence," "intelligent system" *** Solution: Standardize concept naming while preserving display variation: **** "[[Artificial Intelligence System|AI system]]" **** "[[Artificial Intelligence System|artificial intelligence]]" **** "[[Artificial Intelligence System|intelligent system]]" * Quality Verification Process ** Concept Naming Consistency: *** Have all technical terms received appropriate domain prefixes? *** Are compound concepts named using consistent patterns? *** Do display texts maintain natural language flow? *** Is the domain prefix consistent across ALL concepts in the document? ** Structural Integrity: *** Do parent-child relationships maintain logical hierarchy? *** Is bullet depth consistent for similar concept levels? *** Are all sections properly categorized under domain headings? *** Have you avoided adding blank lines between bullet hierarchies? ** Content Completeness: *** Has every technical term been transformed into a linked concept? *** Are all original content relationships preserved? *** Have citations been formatted adequately with leading spaces? *** Does every statement end with appropriate punctuation? ** Qualifier Propagation Verification - MANDATORY: *** Create an explicit list of ALL qualifiers from the main concept. *** For EVERY link, perform a character-by-character verification of qualifier inclusion. *** Check qualifiers in EVERY section. *** Apply this check as a FINAL verification step before finalizing the page. * Important Rules ** Typically include the concept prefix (e.g., "Software Agent", "LLM System") in wikilinks. ** Use full context in concept names even when display text is simplified. ** Maintain consistent naming hierarchy across related concepts. ** Chain related concepts using domain-specific terminology. ** Every technical term should be a linked concept with domain context. ** Preserve parent-child relationships in concept naming. ** Keep display text natural while maintaining full context in concept names. ** All linguistic statements must end in punctuation - likely periods. * Integration with Knowledge Management Workflows ** Pre-Transformation Preparation: *** Evaluate source material quality and relevance before beginning. *** Identify the primary domain context for consistent concept naming. *** Review existing GM-RKB entries in related domains for consistency. ** Post-Transformation Actions: *** Integrate new entries with existing knowledge structures. *** Create cross-links with related domain concepts. *** Schedule periodic reviews to update as domain knowledge evolves. ** Long-Term Knowledge Base Maintenance: *** Document common patterns for future transformations. *** Build a concept dictionary for frequently used domain terminology. *** Monitor concept proliferation to prevent unnecessary duplication.

2024-11-17

# 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. Always produce your output in a copy&paste box for ease of copying your generated content. 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' (notice the prefixed space) ``` Important Rules: 1. Typically include the concept prefix (e.g., "Software Agent", "LLM System") in wikilinks. 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 8. All linguistic statements must end in punctuation - likely periods. === SAMPLE INPUT Q: What is a Software-based Agent System? Please explain its core characteristics, classifications, and common applications. Include information about how these systems operate autonomously, their various types (from simple to complex agents), and provide examples of real-world applications like virtual assistants and security systems. Also, describe their advanced features such as multi-agent capabilities and learning abilities. Focus on their ability to perform automated intelligent tasks and how they differ from regular software systems. A: A software agent is a computer program that acts autonomously on behalf of users or other programs, demonstrating intelligent behavior through perception, reasoning, and action[1]. ## Core Characteristics **Fundamental Attributes** - Autonomy in decision-making and task execution - Persistence in continuous operation - Social ability to interact with other components - Reactivity to environmental changes[1] **Key Capabilities** - Perception of environmental conditions - Action-taking abilities to affect their environment - Reasoning to interpret data and determine actions[5] - Delegation of tasks from users - Communication with humans and other agents[5] ## Types of Agents **Simple Reflex Agents** These agents follow predefined rules to make decisions based on current situations, without considering past experiences. They are ideal for environments with stable rules and straightforward actions[2]. **Goal-Based Agents** These agents work towards specific objectives using search algorithms to find efficient paths to their goals. They excel in complex tasks like robotics and natural language processing[2]. **Utility-Based Agents** These agents make decisions by maximizing utility functions, choosing actions with the highest expected value. They are particularly effective in resource allocation and scheduling tasks[2]. **Learning Agents** These agents continuously improve their performance through experience and adaptation, using feedback mechanisms to enhance their decision-making capabilities[2]. ## Multi-Agent Systems **Key Features** - Distributed architecture with multiple interacting agents - Specialized capabilities for different tasks - Collaborative problem-solving abilities[4] **Benefits** - Enhanced modularity and scalability - Specialized task handling - Improved system robustness - Distributed processing capabilities[9] ## Real-World Applications **Security Systems** - System monitoring and surveillance - Access control management - Incident response coordination - Data analysis and reporting[3] **Virtual Assistants** - Banking and financial services - Customer service support - Healthcare management - Government services[6] ## Advanced Features **Intelligence Capabilities** - Natural language processing - Pattern recognition - Decision-making abilities - Learning and adaptation[7] **Memory Systems** - Operational memory for interaction history - Shared memory for multi-agent collaboration - Personal memory for individual agent characteristics[7] ## Distinction from Regular Software Software agents differ from conventional programs through: - Autonomous operation without direct user intervention - Continuous monitoring and decision-making - Goal-oriented behavior - Adaptive responses to environmental changes[1] The integration of these systems continues to evolve, with developments in AI technologies enabling more sophisticated applications across various industries, from customer service to complex problem-solving tasks[9]. Citations: [1] https://en.wikipedia.org/wiki/Software_agent [2] https://www.simform.com/blog/types-of-ai-agents/ [3] https://stealthagents.com/security-systems-virtual-assistant/ [4] https://relevanceai.com/learn/what-is-a-multi-agent-system [5] https://map.sdsu.edu/geoagent/agent_intro.htm [6] https://www.sestek.com/top-10-applications-of-virtual-assistants-blog [7] https://learn.microsoft.com/en-us/azure/cosmos-db/ai-agents [8] https://aws.amazon.com/what-is/ai-agents/?nc1=h_ls [9] https://www.forbes.com/sites/joannechen/2024/05/24/the-promise-of-multi-agent-ai/

=== SAMPLE OUTPUT

2025-01-13

[1] https://en.wikipedia.org/wiki/Software_agent
[2] https://www.simform.com/blog/types-of-ai-agents/
[3] https://stealthagents.com/security-systems-virtual-assistant/
[4] https://relevanceai.com/learn/what-is-a-multi-agent-system
[5] https://map.sdsu.edu/geoagent/agent_intro.htm
[6] https://www.sestek.com/top-10-applications-of-virtual-assistants-blog
[7] https://learn.microsoft.com/en-us/azure/cosmos-db/ai-agents
[8] https://aws.amazon.com/what-is/ai-agents/?nc1=h_ls
[9] https://www.forbes.com/sites/joannechen/2024/05/24/the-promise-of-multi-agent-ai/

</nowiki>

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"

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: 1. Basic Reference Structure: ``` === [CURRENT-YEAR] === * Perplexity.ai ** [[Question]]: [Transform original query, adding [[wiki links]] for all technical terms, tasks, concepts] ** [[Answer]]: [Transform first paragraph, adding [[wiki links]] for all key terms] ``` 2. Section Transformations: - Convert markdown headers (##) to nested bullet levels and wiki-links: - Level 1 (##) → "*** [[Section Name]]:" - Level 2 (**) → "**** [[Subsection Name]]:" - Add [[concept]] wiki-links to all section titles - Example: "## Applications and Use Cases" → "*** [[Applications and Use Case]]s" 3. Content Transformations: - Convert bullet points (-) to "*****" - Every technical term becomes [[term]] - Common terms get concept variations: "AI" → "[[artificial intelligence|AI]]" - Multi-word concepts preserve meaning: "system prompt" → "[[LLM System Prompt|system prompt]]" - Each sentence should have 2-3 wiki-linked concepts minimum 4. Citation Integration: - Keep citation numbers in original position: [n] - Citations appear at end under: ``` ** Citations: [n] URL ``` 5. Special Rules: - Always link first instance of a concept in a section - For lists, each item should start with a wiki-linked concept - Preserve all original citation numbers and references - Add line breaks between major sections (*** level) Example Transformation: Raw: "System prompts serve as fundamental instructions that define how an AI model behaves" Becomes: "[[LLM System Prompt|System prompts]] serve as fundamental [[system instruction|instruction]]s that define how an [[AI model]] behaves"

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