PlexPt/awesome-chatgpt-prompts-zh
Awesome Chatgpt Prompts Zh
This project is a community-driven library of structured text inputs designed to guide large language models into specific roles, behaviors, and operational modes. It functions as a comprehensive repository of prompt engineering resources, providing reusable templates that allow users to override default model tendencies and enforce domain-specific response patterns through instruction-following logic.
The collection distinguishes itself by offering specialized persona-based directives that constrain model output to simulate professional experts or functional technical environments. By utilizing few-shot conditioning and system-level instructions, the library enables users to enforce strict output schemas, suppress conversational filler, and maintain the illusion of persistent environments, such as simulated terminal interfaces or programming interpreters.
The repository covers a broad capability surface, including resources for creative content generation, automated workflow assistance, and technical development tasks. These templates are categorized to support diverse applications ranging from software engineering and database management to academic writing and linguistic analysis. The project serves as a practical guide for implementing advanced interaction techniques to improve the quality and relevance of model responses.
Features
- Prompt Engineering Libraries - Curated community templates enable users to interact with conversational models more effectively through structured input patterns.
- Instruction-Following Layers - Logic-driven prompts interpret user constraints to enforce consistent response patterns and override default model tendencies.
- Prompt Collections - Aggregated text inputs guide language models into specific roles and behaviors, simplifying complex interaction tasks.
- Awesome List - A community-curated directory that catalogs and links out to other open-source projects, rather than a standalone tool you run yourself.
- Workflows, Methodologies & Prompts - Structured methodologies help users define operational modes and workflows to steer model behavior toward specific goals.
- Agentic Systems Frameworks - Predefined persona directives allow users to configure specialized, context-aware assistance for complex agentic tasks.
- Generative Art Prompts - Creative prompt libraries assist in generating descriptive imagery and artistic content through refined, task-specific instructions.
- AI Workflow Patterns - Standardized interaction patterns facilitate complex workflows including code generation, database querying, and terminal command simulation.
- Few-Shot Persona Conditioning - Context blocks condition model output by providing example-based constraints that shape the persona and interaction style.
- Output Formatting Constraints - Formatting templates enforce strict output schemas while suppressing conversational filler to ensure clean, usable data responses.
- Domain-Specific - Specialized instructions provide tailored guidance for professional fields ranging from software development to linguistic analysis.
- Prompt Engineering - Chinese-language guide and prompt collection for ChatGPT.
- Research Collections - Chinese-language collection of prompt engineering examples.
- Educational Resources - Collection of prompt strategies for optimizing model interactions.
- Knowledge & Reference - Extensive library of conversational examples and prompt patterns.
- Aprendizaje y referencia - Extensive repository of conversational examples and prompt references.
- Prompt Engineering - Chinese-language collection of optimized model prompts.
- Prompt Engineering Patterns - Methodological strategies for prompt construction improve the reliability and performance of model interactions through proven structural patterns.
- Persona Instruction Sets - Behavioral directives constrain model output to simulate specific professional experts or functional technical environments.
- PHP Interpreters - System prompts transform language models into functional interpreters capable of executing and returning output for specific programming languages.
