system-prompt: A Collection of System Prompts and AI Cores for Large Language Models

system-prompt is an open-source collection of system prompts and AI cores for large language models. It provides optimized prompt templates for developers and researchers, helping to lower the barrier to writing prompts and better control and guide the behavior and output quality of LLMs. The project covers various application scenarios, role settings, and best practices, serving as an important reference resource for building high-quality LLM applications.

System prompts are key components in LLM applications, defining the model's role, behavioral guidelines, output format, and constraints, directly affecting response quality and consistency. However, writing high-quality prompts requires skills and experience, and developers often spend a lot of time trial and error. The system-prompt project aims to lower this barrier by providing validated prompt templates, helping developers quickly build high-quality AI applications.

system-prompt is an open-source repository containing system prompts and AI cores for various purposes:

• System Prompts: Core content, including role-based (defining specific roles like technical experts), task-based (optimized for tasks such as code review), and constraint-based (defining boundaries like safety guidelines);
• AI Cores: More complex and structured, including multi-stage processing logic, state management, tool call definitions, and decision logic, providing a runtime environment similar to an 'operating system' for the model.

Key principles summarized from the project:

1. Clear Role Definition: Specify professional identity, target audience, and communication style;
2. Specific Task Instructions: Explain input processing, steps, output format, and quality standards;
3. Explicit Constraints: Define content, behavior, and format limitations;
4. Effective Example Guidance: Provide positive examples, negative examples, and boundary cases;
5. Continuous Iterative Optimization: Collect feedback, conduct A/B testing, and manage versions.

The project can be applied in:

• Rapid Prototype Development: Directly use templates to shorten prototype time;
• Production System Optimization: Refer to best practices to improve output quality;
• Education and Training: Serve as learning material for beginners in prompt engineering;
• Community Collaboration: Users can submit prompts, improve existing content, and share experiences.

Challenges faced in the field:

• Model Dependency: Different LLMs respond to prompts with significant differences;
• Prompt Fragility: Minor changes may significantly affect output;
• Safety and Alignment: Need to prevent risks such as prompt injection;
• Evaluation Difficulty: Lack of a universal framework for evaluating prompt quality.

Future development directions for the project and the field:

• Model Adaptation: Provide optimized versions for different LLMs;
• Automatic Optimization: Combine automated technologies to improve prompt effectiveness;
• Domain Expansion: Cover vertical fields such as healthcare and law;
• Tool Integration: Integrate with development tools to provide functions like version management;
• Standardization Efforts: Promote industry best practices and evaluation standards.

The system-prompt project highlights the key role of system prompt design in LLM application development, lowering the barrier to building high-quality applications. As LLM technology evolves, prompt engineering, as a bridge connecting model capabilities and practical applications, becomes increasingly important and is an essential skill for LLM practitioners.