LLM-based System System Prompt

An LLM-based System System Prompt is a large language model prompt that provides instructions about the AI assistant's role, behavior, knowledge, and limitations.

• Context:
  • ...
  • It can range from being a Simple LLM System Prompt to being a Complex LLM System Prompt, depending on its LLM instruction complexity.
  • It can range from being a General-Purpose LLM System Prompt to being a Domain-Specific LLM System Prompt, depending on its LLM application scope.
  • It can range from being a Fixed LLM System Prompt to being a Dynamic LLM System Prompt, depending on its LLM prompt adaptability.
  • It can range from being a Human-Generated System Prompt to being a Auto-Generated LLM System Prompt, depending on its LLM system prompt creation method.
  • It can range from being a Unstructured LLM System Prompt to being an Structured LLM Prompt, depending on the level of LLM system prompt organization.
  • It can range from being a Single-Task System Prompt to being a Multi-Task System Prompt, depending on the task breadth and complexity it supports.
  • It can range from being a Placeholder-Free LLM System Prompt to being an Annotated LLM System Prompt, depending on its use of annotation placeholders for contextual or task-specific guidance.
  • ...
  • It can include LLM System Knowledge Scope.
  • It can include LLM System Limitations.
  • It can include LLM System Role Definitions.
  • It can include LLM System Behavioral Constraints.
  • It can include LLM System Base Definition.
  • It can include LLM System Response Format Specification.
  • It can include LLM System Ethical Guidelines.
  • It can be created by an LLM System Designer.
  • ...
• Example(s):
  • General AI Assistant Prompts (for General AI assistants), such as:
    • Claude Chatbot LLM System Prompt (for Claude chatbot), "You are Claude, created by Anthropic, focused on being helpful while being direct.".
  • Specialized AI Assistant Prompts (for Domain-Specific AI assistants), such as:
    • Math Tutor Prompt (for Math tutoring AI assistant), "You are a math tutor who explains concepts step by step.".
    • Coding Assistant Prompt (for Coding AI assistant), "You are a coding assistant who provides well-documented solutions.".
  • Role-Playing AI Prompts (for Role-Playing AI assistants), such as:
    • French Language Teacher Prompt (for French language tutor AI assistant), "You are a French language teacher helping with pronunciation.".
    • Historical Figure Role-Playing Prompt (for 18th-century historical figure AI assistant), "You are a historical figure from the 18th century.".
  • Structured Knowledge Management System Prompts (for Knowledge Base Management AI assistants), such as:
    • GM-RKB Assistant System Prompt (for GM-RKB).
    • Enterprise Wiki Management Prompt (for Corporate Knowledge Base Management), "You are an AI assistant managing an enterprise wiki. Your role is to create consistent and comprehensive knowledge pages, ensuring compliance with internal style guides, proper categorization, and up-to-date information.".
  • Conversational Chatbot System Prompts, such as:
    • E-Commerce Support Chatbot System Prompt: “You are a customer service assistant providing quick, helpful answers about product availability and delivery timelines. Maintain a friendly and professional tone.”.
  • Back-End LLM-based System Prompts, such as:
    • NLU Data Extraction Prompt: “Extract named entities (Person, Organization, Date, Location) from the given text. Return the results in JSON format.”.
  • LLM-based Utility System Prompts, such as:
    • GM-RKB Perplexity Reference Citation Entry System Prompt.
  • ...
• Counter-Example(s):
  • User Prompts, which provide specific tasks rather than system-level instructions.
  • LLM Fine-Tuning Instructions, which modify model weights rather than providing runtime guidance.
  • Model Configuration Parameters, which set technical variables rather than behavioral guidelines.
• See: LLM Prompt, AI Assistant Role, System Instruction, Prompt Engineering.

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References

2024-11-16

• Perplexity.ai
  • Question: Can you explain what a system prompt is in AI language models? I'd like to understand its core components, implementation methods, and practical applications. Please include information about how system prompts define an AI system's behavior, knowledge boundaries, and response patterns. Also, discuss the difference between simple and complex prompts, and explain how they're used in various contexts like general assistance, specialized tasks, and role-playing scenarios. Finally, touch on best practices for writing effective system prompts and their technical limitations.
  • Answer: System prompts serve as fundamental instructions that define how an AI model behaves and interacts with users. Let's explore their key aspects and applications.
    • Basic Structure: A system prompt typically contains behavioral guidelines, role definitions, and operational parameters that shape the AI system's responses [2]. These instructions remain active throughout the interaction, establishing a consistent framework for the AI system's behavior [3].
    • Key Elements:
      • Context setting and behavioral guidelines
      • Response style specifications
      • Operational constraints
      • Ethical boundaries [3]
  • Applications and Use Cases
    • General Applications - System prompts are crucial in various scenarios:
      • Customer service interactions
      • Educational tutoring
      • Technical support
      • Content generation
      • Specialized domain expertise [2] [4]
    • Role-Based Implementations
    • The system prompt can define specific roles and personalities for the AI system, though recent research suggests that adding personas doesn't necessarily improve performance on objective tasks [5].
  • Technical Considerations
    • Implementation Methods - System prompts can be implemented through:
      • API calls with dedicated system message parameters
      • Pre-defined behavioral frameworks
      • Task-specific instruction sets [6]
    • Best Practices:
      • Write clear, concise instructions
      • Maintain ethical guidelines
      • Update system prompts periodically based on performance
      • Provide specific context for specialized tasks [3]
  • Limitations and Challenges
    • Technical Constraints:
      • The LLM-System prompt effectiveness varies depending on task complexity.
      • LLM-System performance may not always improve with more detailed system prompts.
      • Automatic selection of optimal system prompts remains challenging [5].
    • Practical Considerations:
      • System prompts should balance flexibility with consistency.
      • Regular updates may be needed to maintain relevance.
      • Different models may respond differently to the same system prompt [4].
  • Citations:

[1] https://www.reddit.com/r/PromptEngineering/comments/1f7mltz/ai_system_prompts_compared/
[2] https://www.promptlayer.com/glossary/system-prompt
[3] https://www.nebuly.com/blog/llm-system-prompt-vs-user-prompt
[4] https://promptengineering.org/system-prompts-in-large-language-models/
[5] https://arxiv.org/abs/2311.10054
[6] https://cloud.google.com/vertex-ai/generative-ai/docs/learn/prompts/introduction-prompt-design
[7] https://github.com/mustvlad/ChatGPT-System-Prompts
[8] https://community.openai.com/t/what-should-be-included-in-the-system-part-of-the-prompt/515763

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