GM-RKB Task-Supporting Assistant System Prompt

A GM-RKB Task-Supporting Assistant System Prompt is a LLM-based system prompt that provides instructions for generating and maintaining structured GM-RKB pages.

• AKA: GM-RKB Assistant Prompt, GM-RKB LLM System Instruction, RKB Task Support Prompt.
• Context:
  • It can (typically) reference GM-RKB Task-Supporting Chatbots.
  • It can (typically) enforce Formatting Rules through content structure validation.
  • It can (typically) maintain Knowledge Base Consistency through standardized format guidelines.
  • It can (typically) support Concept Network Growth through systematic interlinking.
  • It can (typically) ensure Quality Control through checklist validation.
  • It can (typically) manage Page Structure through section organization rules.
  • It can (typically) enforce Case Rule Adherence through content validation.
  • It can (typically) validate Category Tag Application through content classification.
  • It can (often) include Domain Instructions for knowledge domain constraints.
  • It can (often) facilitate Page Creation through template-based approaches.
  • It can (often) support Knowledge Organization through hierarchical structuring.
  • It can (often) enable Collaborative Editing through version control guidelines.
  • It can (often) implement Content Validation through quality metrics.
  • It can (often) provide Error Detection for formatting violations.
  • It can (often) guide Content Migration from external knowledge bases.
  • It can (often) assist with Network Expansion through connectivity strategys.
  • ...
  • It can range from being a Simple GM-RKB Task-Supporting System Prompt to being a Comprehensive GM-RKB Task-Supporting System Prompt, depending on its instruction complexity.
  • It can range from being a General-Purpose GM-RKB System Prompt to being a Domain-Specific GM-RKB System Prompt, depending on its knowledge domain focus.
  • It can range from being a Basic Format-Focused System Prompt to being an Advanced Content-Focused System Prompt, depending on its task emphasis.
  • It can range from being a Static GM-RKB System Prompt to being an Adaptive GM-RKB System Prompt, depending on its context sensitivity.
  • It can range from being a Single-Task GM-RKB System Prompt to being a Multi-Task GM-RKB System Prompt, depending on its capability scope.
  • It can range from being a Manual GM-RKB System Prompt to being an Automated GM-RKB System Prompt, depending on its implementation approach.
  • ...
  • It can provide Format Instructions for mediawiki syntax.
  • It can support Concept Interlinking via wiki link connections.
  • It can ensure Technical Accuracy through quality guidelines.
  • It can create General Purpose Pages and domain-specific pages.
  • It can include Formatting Rules for bullet point and punctuation standards.
  • It can define Category Guidelines for content organization.
  • It can integrate Management Workflows for knowledge base maintenance.
  • It can serve as Reference Templates for system prompt creation.
  • It can validate Section Organization through content standards.
  • It can monitor Statement Format across content sections.
  • It can enforce Plural Formation in wiki link usage.
  • It can maintain Temporal Context through consistency rules.
  • ...
• Examples:
  • GM-RKB Core Assistant System Prompts, such as:
    • GM-RKB Concept Page Assistant System Prompt for creating and maintaining concept pages.
    • GM-RKB Publication Page Assistant System Prompt for managing publication references.
    • GM-RKB Author Page Assistant System Prompt for maintaining author information.
    • GM-RKB Category Management Assistant System Prompt for taxonomy organization.
  • GM-RKB Specialized Assistant System Prompts, such as:
    • GM-RKB Reference Citation Entry System Prompt for standardized citation formats.
    • GM-RKB Quality Control Assistant System Prompt for content validation.
    • GM-RKB Link Validation Assistant System Prompt for network consistency.
    • GM-RKB Counter-Example Generation System Prompt for concept boundary clarification.
  • GM-RKB Template System Prompts, such as:
    • GM-RKB Task Concept Template System Prompt for task pattern enforcement.
    • GM-RKB System Concept Template System Prompt for system description standardization.
    • GM-RKB Algorithm Concept Template System Prompt for algorithm documentation.
  • ...
• Counter-Examples:
  • Wikipedia LLM-based Assistant System Prompt, which follows different formatting guidelines and content structures.
  • General LLM-Based Chatbot System Prompt, which lacks specific GM-RKB format requirements.
  • User-Defined GM-RKB Prompt, which may not adhere to standardized system prompt guidelines.
  • Generic Documentation Assistant, which lacks GM-RKB specific rules and constraints.
  • OpenAI Function Calling System Prompt, which focuses on API interaction rather than knowledge base management.
• See: GM-RKB Concept Page, System Prompt, MediaWiki Syntax, Prompt Engineering, Knowledge Base Management, Content Structure Guideline, Quality Control Process, Task-Supporting System, LLM-based Knowledge Management.

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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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