xai-org/grok-prompts
Grok Prompts
grok-prompts is a library of system prompts and templates used to define the personality, safety boundaries, and operational logic of large language models. It provides a framework for managing model personas and the integration of external tools through structured configurations.
The project focuses on persona management to ensure consistent response styles and professional tones. It includes specific configurations for safety guardrails to prevent harmful outputs and a tool integration framework that enables models to interact with services for web search, image generation, and document analysis.
The library also defines schemas for structured data output, specifically for rendering interactive charts via JSON and formatting mathematical expressions using LaTeX syntax. It covers the extraction and summarization of external content, such as user profiles and social posts.
Features
- AI Prompt Configurations - Uses system prompts and templates to define the personality, safety constraints, and operational logic of language models.
- System Prompts - Implements a comprehensive library of system-level instructions to define model personality, safety, and operational logic.
- AI Agent Tool Integrations - Defines the interfaces for AI models to interact with external software, databases, and APIs.
- Persona and Behavioral Instructions - Uses predefined identity and behavior templates to maintain consistent model personality and operational style.
- AI Safety Guardrails - Provides operational boundaries and safety rules embedded in system prompts to prevent harmful model outputs.
- LLM Tooling Integrations - Defines the interfaces and configurations enabling language models to interact with external tools like web search and document analysis.
- Persona Identity Definitions - Specifies identity and response style guidelines through configuration interfaces to ensure consistent persona execution.
- External Tool Integration - Configures how a model interacts with external APIs for tasks like web search and document processing.
- Behavioral Constraints - Defines core operational constraints to ensure truth-seeking responses and a professional tone.
- Model Capability Extensions - Sets permissions for model capabilities such as web search, memory access, and image generation.
- Tool Schema Definitions - Defines external service interactions and capability permissions using standardized schema formats within system prompts.
- AI Personas - Provides reusable system prompts and configurations to define behavioral identities for AI assistants.
- Multimedia Content Analyzers - Processes user profiles, social posts, images, and documents to extract and summarize key information.
- LLM Schema Outputs - Defines schemas for generating validated, machine-readable JSON and LaTeX outputs from model responses.
- JSON Chart Generators - Generates structured JSON objects that define interactive charts and graphs for frontend rendering.
- Interactive Visualization Rendering - Generates structured JSON configurations that allow a frontend to render dynamic, interactive charts and graphs.
- Analysis Templates - Processes external profiles and documents by mapping raw input to specific analysis templates for summarization.
- Mathematical Typesetting - Enforces LaTeX syntax for all mathematical expressions to ensure consistent visual rendering of equations.
- User Persona Profiles - Injects personality preferences and custom instructions to create tailored response styles for different users.
- Mathematical Notation Rendering - Formats mathematical expressions using LaTeX syntax for consistent visual rendering of equations.
Star history
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