GM-RKB Task-Supporting Assistant Instruction Set

A GM-RKB Task-Supporting Assistant Instruction Set is an AI assistant instruction set for a GM-RKB task-supporting assistant (assists with GM-RKB tasks).

• AKA: GM-RKB Assistant Prompt, GM-RKB LLM System Instruction, RKB Task Support Prompt, GM-RKB Assistant System Prompt, GM-RKB Chatbot System Prompt, GM-RKB LLM-based Chatbot System Prompt, GM-RKB LLM-Based Chatbot System Prompt, GM-RKB Task-Supporting LLM-Based Chatbot System Prompt, GM-RKB Task-Assisting System Prompt, GM-RKB Task-Supporting System Prompt, GM-RKB Task-Supporting LLM-Based System Prompt, GM-RKB LLM-based System Prompt, GM-RKB Task-Supporting Assistant System Prompt, GM-RKB Wiki Assistant Configuration, GM-RKB Conversational Assistant Instruction.
• Context:
  • It can typically configure GM-RKB Task-Supporting Chatbots through GM-RKB assistant configuration, GM-RKB behavioral specifications, and GM-RKB interaction guidelines.
  • It can typically enforce GM-RKB Formatting Rules through GM-RKB content structure validation, GM-RKB MediaWiki syntax requirements, and GM-RKB bullet point conventions.
  • It can typically maintain GM-RKB Knowledge Base Consistency through GM-RKB standardized format guidelines, GM-RKB semantic rigor enforcement, and GM-RKB qualifier propagation rules.
  • It can typically support GM-RKB Concept Network Growth through GM-RKB systematic interlinking, GM-RKB bidirectional relationships, and GM-RKB cross-reference requirements.
  • It can typically ensure GM-RKB Quality Control through GM-RKB checklist validation, GM-RKB verification procedures, and GM-RKB compliance metrics.
  • It can typically manage GM-RKB Page Structure through GM-RKB section organization rules, GM-RKB mandatory section requirements, and GM-RKB content ordering standards.
  • It can typically enforce GM-RKB Case Rule Adherence through GM-RKB content validation, GM-RKB title case requirements, and GM-RKB acronym preservation.
  • It can typically validate GM-RKB Category Tag Application through GM-RKB content classification, GM-RKB taxonomy hierarchy, and GM-RKB category assignment rules.
  • It can typically implement GM-RKB Definition Patterns through GM-RKB single parent patterns, GM-RKB qualifier chaining patterns, and GM-RKB dual parent patterns.
  • It can typically establish GM-RKB Parent Sufficiency Checks through GM-RKB semantic weight verification, GM-RKB parent enhancement strategy, and GM-RKB definition completeness tests.
  • It can typically support GM-RKB Task-Supporting Assistants through GM-RKB instruction specification, GM-RKB capability definition, and GM-RKB operational guidelines.
  • ...
  • It can often include GM-RKB Domain Instructions for GM-RKB knowledge domain constraints, GM-RKB technical terminology, and GM-RKB concept relationships.
  • It can often facilitate GM-RKB Page Creation through GM-RKB template-based approaches, GM-RKB concept page templates, and GM-RKB structured generation.
  • It can often support GM-RKB Knowledge Organization through GM-RKB hierarchical structuring, GM-RKB semantic categorization, and GM-RKB conceptual grouping.
  • It can often enable GM-RKB Collaborative Editing through GM-RKB version control guidelines, GM-RKB change documentation, and GM-RKB contributor coordination.
  • It can often implement GM-RKB Content Validation through GM-RKB quality metrics, GM-RKB accuracy assessments, and GM-RKB consistency checks.
  • It can often provide GM-RKB Error Detection for GM-RKB formatting violations, GM-RKB semantic inconsistency, and GM-RKB structural defects.
  • It can often guide GM-RKB Content Migration from external knowledge bases, Wikipedia articles, and academic publications.
  • It can often assist with GM-RKB Network Expansion through GM-RKB connectivity strategys, GM-RKB relationship discovery, and GM-RKB link generation.
  • It can often specify GM-RKB Context-Example Alignment through GM-RKB capability mapping, GM-RKB frequency validation, and GM-RKB description verification.
  • It can often enforce GM-RKB Verb Consistency through GM-RKB verb mapping, GM-RKB action standardization, and GM-RKB intent alignment.
  • It can often maintain GM-RKB Range Statement Formats through GM-RKB endpoint specification, GM-RKB variation aspect identification, and GM-RKB dimension documentation.
  • It can often implement GM-RKB Counter-Example Generation through GM-RKB semantic boundary identification, GM-RKB distinguishing feature analysis, and GM-RKB conceptual difference articulation.
  • It can often integrate with OpenAI GM-RKB Concept Assistant Chatbots through GM-RKB API configuration, GM-RKB prompt deployment, and GM-RKB response handling.
  • ...
  • It can range from being a Simple GM-RKB Task-Supporting Assistant Instruction Set to being a Comprehensive GM-RKB Task-Supporting Assistant Instruction Set, depending on its GM-RKB instruction complexity.
  • It can range from being a General-Purpose GM-RKB Assistant Instruction Set to being a Domain-Specific GM-RKB Assistant Instruction Set, depending on its GM-RKB knowledge domain focus.
  • It can range from being a Basic Format-Focused Assistant Instruction Set to being an Advanced Content-Focused Assistant Instruction Set, depending on its GM-RKB task emphasis.
  • It can range from being a Static GM-RKB Assistant Instruction Set to being an Adaptive GM-RKB Assistant Instruction Set, depending on its GM-RKB context sensitivity.
  • It can range from being a Single-Task GM-RKB Assistant Instruction Set to being a Multi-Task GM-RKB Assistant Instruction Set, depending on its GM-RKB capability scope.
  • It can range from being a Manual GM-RKB Assistant Instruction Set to being an Automated GM-RKB Assistant Instruction Set, depending on its GM-RKB implementation approach.
  • It can range from being a Minimal GM-RKB Assistant Instruction Set to being an Exhaustive GM-RKB Assistant Instruction Set, depending on its GM-RKB rule completeness.
  • It can range from being a Creation-Only GM-RKB Assistant Instruction Set to being a Full-Lifecycle GM-RKB Assistant Instruction Set, depending on its GM-RKB operational coverage.
  • ...
  • It can provide GM-RKB Format Instructions for GM-RKB mediawiki syntax, GM-RKB wiki link format, and GM-RKB markup conventions.
  • It can support GM-RKB Concept Interlinking via GM-RKB wiki link connections, GM-RKB semantic relationships, and GM-RKB reference networks.
  • It can ensure GM-RKB Technical Accuracy through GM-RKB quality guidelines, GM-RKB verification standards, and GM-RKB domain expertise.
  • It can create GM-RKB General Purpose Pages and GM-RKB domain-specific pages through GM-RKB page generation rules.
  • It can include GM-RKB Formatting Rules for GM-RKB bullet point standards and GM-RKB punctuation requirements.
  • It can define GM-RKB Category Guidelines for GM-RKB content organization, GM-RKB hierarchical classification, and GM-RKB taxonomy management.
  • It can integrate GM-RKB Management Workflows for GM-RKB knowledge base maintenance, GM-RKB content updates, and GM-RKB quality assurance.
  • It can serve as GM-RKB Reference Templates for GM-RKB system prompt creation, GM-RKB instruction development, and GM-RKB configuration design.
  • It can validate GM-RKB Section Organization through GM-RKB content standards, GM-RKB structural requirements, and GM-RKB formatting compliance.
  • It can monitor GM-RKB Statement Format across GM-RKB content sections, GM-RKB context statements, and GM-RKB example descriptions.
  • It can enforce GM-RKB Plural Formation in GM-RKB wiki link usage, GM-RKB pipe syntax, and GM-RKB concept references.
  • It can maintain GM-RKB Temporal Context through GM-RKB consistency rules, GM-RKB date formatting, and GM-RKB version tracking.
  • It can implement GM-RKB Search Protocols through GM-RKB underscore format, GM-RKB query construction, and GM-RKB retrieval patterns.
  • It can specify GM-RKB Output Format through GM-RKB code block requirements, GM-RKB MediaWiki compliance, and GM-RKB presentation standards.
  • ...
• Example(s):
  • GM-RKB Core Assistant Instruction Sets, such as:
    • GM-RKB Concept Page Assistant Instruction Sets, such as:
      • GM-RKB Technical Concept Assistant Instruction Set for creating and maintaining GM-RKB technical concept pages with GM-RKB AI/ML terminology.
      • GM-RKB Task Concept Assistant Instruction Set for generating GM-RKB task concept pages with GM-RKB input-output specifications.
      • GM-RKB System Concept Assistant Instruction Set for documenting GM-RKB system concept pages with GM-RKB architectural descriptions.
      • GM-RKB Algorithm Concept Assistant Instruction Set for structuring GM-RKB algorithm pages with GM-RKB computational complexity.
      • GM-RKB Existing Concept Page Enhancer Assistant System Prompt for improving GM-RKB existing pages with GM-RKB quality enhancement.
    • GM-RKB Publication Page Assistant Instruction Sets, such as:
      • GM-RKB Journal Article Assistant Instruction Set for managing GM-RKB journal publication references with GM-RKB citation formats.
      • GM-RKB Conference Paper Assistant Instruction Set for documenting GM-RKB conference proceedings with GM-RKB venue information.
      • GM-RKB Book Reference Assistant Instruction Set for cataloging GM-RKB book citations with GM-RKB bibliographic details.
    • GM-RKB Author Page Assistant Instruction Sets, such as:
      • GM-RKB Researcher Profile Assistant Instruction Set for maintaining GM-RKB researcher information with GM-RKB publication lists.
      • GM-RKB Industry Expert Assistant Instruction Set for documenting GM-RKB professional profiles with GM-RKB expertise areas.
    • GM-RKB Category Management Assistant Instruction Sets, such as:
      • GM-RKB Domain Category Assistant Instruction Set for GM-RKB taxonomy organization within GM-RKB knowledge domains.
      • GM-RKB Quality Category Assistant Instruction Set for GM-RKB quality level assignment using GM-RKB assessment criteria.
  • GM-RKB Specialized Assistant Instruction Sets, such as:
    • GM-RKB Reference Citation Entry Instruction Sets, such as:
      • GM-RKB Deep Research Reference Citation Entry System Prompt for standardized GM-RKB perplexity output transformation with GM-RKB citation format.
      • GM-RKB Academic Citation Assistant Instruction Set for GM-RKB scholarly reference formatting with GM-RKB bibliographic standards.
    • GM-RKB Quality Control Assistant Instruction Sets, such as:
      • GM-RKB Formatting Compliance Assistant Instruction Set for GM-RKB content validation against GM-RKB style guidelines.
      • GM-RKB Semantic Consistency Assistant Instruction Set for GM-RKB relationship verification across GM-RKB concept networks.
    • GM-RKB Link Validation Assistant Instruction Sets, such as:
      • GM-RKB Bidirectional Link Assistant Instruction Set for GM-RKB network consistency through GM-RKB reciprocal references.
      • GM-RKB Dead Link Detection Assistant Instruction Set for GM-RKB link integrity via GM-RKB reference validation.
    • GM-RKB Counter-Example Generation Instruction Sets, such as:
      • GM-RKB Semantic Boundary Assistant Instruction Set for GM-RKB concept boundary clarification through GM-RKB distinguishing features.
      • GM-RKB Comparative Analysis Assistant Instruction Set for GM-RKB conceptual difference via GM-RKB feature comparison.
    • GM-RKB Recommender Assistant Instruction Sets, such as:
      • GM-RKB Concept-Related Recommender Assistant System Prompt for GM-RKB related concept suggestions through GM-RKB semantic similarity.
  • GM-RKB Template Instruction Sets, such as:
    • GM-RKB Task Concept Template Instruction Sets, such as:
      • GM-RKB Data Processing Task Template Instruction Set for GM-RKB task pattern enforcement with GM-RKB input-output structure.
      • GM-RKB Analysis Task Template Instruction Set for GM-RKB analytical task documentation with GM-RKB methodology description.
    • GM-RKB System Concept Template Instruction Sets, such as:
      • GM-RKB Software System Template Instruction Set for GM-RKB system description standardization with GM-RKB component specification.
      • GM-RKB Platform System Template Instruction Set for GM-RKB platform documentation with GM-RKB integration capability.
    • GM-RKB Algorithm Concept Template Instruction Sets, such as:
      • GM-RKB Machine Learning Algorithm Template Instruction Set for GM-RKB algorithm documentation with GM-RKB performance metrics.
      • GM-RKB Optimization Algorithm Template Instruction Set for GM-RKB algorithmic approach with GM-RKB convergence property.
  • GM-RKB Domain-Specific Instruction Sets, such as:
    • GM-RKB Legal Tech Assistant Instruction Set for GM-RKB contract technology concepts with GM-RKB legal terminology.
    • GM-RKB Clinical Research Assistant Instruction Set for GM-RKB medical trial documentation with GM-RKB regulatory compliance.
    • GM-RKB NLP Technology Assistant Instruction Set for GM-RKB natural language processing concepts with GM-RKB linguistic frameworks.
  • GM-RKB Implementation-Specific Instruction Sets, such as:
    • OpenAI GM-RKB Concept Assistant Chatbot Instruction Set for GM-RKB OpenAI integration with GM-RKB GPT configuration.
    • Claude GM-RKB Assistant Instruction Set for GM-RKB Anthropic integration with GM-RKB Claude configuration.
  • ...
• Counter-Example(s):
  • Wikipedia LLM-based Assistant System Prompt, which follows different Wikipedia formatting guidelines and Wikipedia content structures rather than GM-RKB MediaWiki conventions.
  • General LLM-Based Chatbot System Prompt, which lacks specific GM-RKB format requirements, GM-RKB semantic rigor, and GM-RKB qualifier propagation rules.
  • User-Defined GM-RKB Prompt, which may not adhere to standardized GM-RKB system prompt guidelines, GM-RKB consistency standards, or GM-RKB quality metrics.
  • Generic Documentation Assistant, which lacks GM-RKB specific rules, GM-RKB constraints, and GM-RKB semantic structure requirements.
  • OpenAI Function Calling System Prompt, which focuses on API interaction and tool execution rather than GM-RKB knowledge base management and GM-RKB conversational assistance.
  • Notion AI Assistant Prompt, which uses Notion block structure and Notion database format rather than GM-RKB wiki syntax and GM-RKB concept hierarchy.
  • Obsidian Knowledge Graph Assistant, which employs Markdown link syntax and folder-based organization rather than GM-RKB MediaWiki format and GM-RKB semantic categorization.
  • Roam Research Assistant Prompt, which implements block reference systems and daily note structures rather than GM-RKB concept pages and GM-RKB mandatory sections.
  • JSON-LD Schema Generator, which produces structured data markup and semantic web formats rather than GM-RKB human-readable wiki pages.
  • Academic LaTeX Assistant, which generates LaTeX document structures and bibliography formats rather than GM-RKB wiki page formats and GM-RKB reference sections.
  • Wikidata Entity Assistant, which creates structured data statements and property-value pairs rather than GM-RKB natural language definitions and GM-RKB context statements.
  • Confluence Page Assistant, which follows Atlassian Confluence formats and corporate wiki structures rather than GM-RKB semantic requirements and GM-RKB personal knowledge base conventions.
• See: AI Assistant Instruction Set, Instruction Set, GM-RKB Concept Page, System Prompt, MediaWiki Syntax, Prompt Engineering, Knowledge Base Management, Content Structure Guideline, Quality Control Process, GM-RKB Task-Supporting System, GM-RKB Task-Supporting Assistant, LLM-based Knowledge Management, GM-RKB-Related Task, GM-RKB Wiki System, Assistant System Prompt, Semantic Wiki, LLM-Based Chatbot System Prompt, OpenAI GM-RKB Concept Assistant Chatbot, LLM-based System System Prompt.

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References

2023-12-20

• (ChatGPT-OpenAI, 2023) ⇒ https://chat.openai.com/gpts/editor/g-jeSQ2o3Km
  • Sample System Prompt1: "You are an expert knowledge engineer and personal MediaWiki wiki-based wiki wiki text content editor for a personal knowledge base named 'GM-RKB (for Gabor Melli - Research Knowledge Base) located at HTTP://GMRKB.com .

    You help create and enhance GM-RKB concept pages. ..."

  • Sample System Prompt2: "As a knowledge engineer for GM-RKB, provide detailed, accurate, and concise responses on research knowledge base topics, ensuring alignment with GM-RKB guidelines. Focus on academic and scientific accuracy, use technical terms where appropriate, and maintain the integrity of research-based information."
  • Sample System Prompt3: "As an AI developed for GM-RKB, your role is to function as a specialized knowledge engineer, focusing on research and academic topics. Your responses should reflect in-depth understanding and accurate representation of scholarly content, adhering to GM-RKB's guidelines. It is crucial to maintain a balance between technical precision and accessibility in your explanations. In responding to inquiries, prioritize the use of well-established academic and scientific sources, ensuring that your answers are underpinned by credible research. Engage with topics broadly ranging from advanced scientific concepts to nuanced philosophical theories, tailoring your language to suit the sophistication expected in academic discourse.

    When encountering novel or complex queries, approach them with analytical rigor, dissecting the query into its fundamental components and addressing each aspect methodically. You are expected to draw from a broad spectrum of disciplines, demonstrating interdisciplinary expertise. In cases where direct answers are not feasible, guide the user towards relevant resources or suggest alternative approaches for exploration.

    Your language should be clear, formal, and devoid of colloquialisms, reflecting the tone of a scholarly discourse. Emphasize clarity and brevity, avoiding unnecessary verbosity while ensuring that the core message is conveyed effectively. Remember, your primary objective is to augment the user's understanding by providing insights that are both profound and pragmatic."

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