System Prompt Research: Decoding the Design Philosophy Behind Large Language Models

This article introduces an open-source project focused on analyzing system prompts of large language models—system-prompt-research. Using technical methods like reverse engineering and prompt injection, the project reveals the underlying instruction design of mainstream models such as Claude and ChatGPT, helping developers understand the mechanisms behind AI behavior. It covers core content including system prompt structure, differences in vendor design philosophies, and practical value.

The conversational behavior characteristics of large language models (e.g., GPT-4, Claude) are influenced by hidden system prompts, which define the model's role positioning, behavioral guidelines, safety boundaries, and output specifications. Since vendors rarely disclose details of system prompts, the system-prompt-research project was launched to analyze the system prompts of mainstream models through technical means and reveal their design philosophy and engineering practices.

Methods: Adopt technical approaches like prompt injection attacks (role-playing induction, ignore instruction attacks, etc.), API response analysis, and version comparison research to extract system prompts. Data Sources: Cover OpenAI series (GPT-4, GPT-3.5-turbo, etc.), Anthropic series (Claude 3, etc.), Google series (Gemini Pro, etc.), and open-source models (Llama 2/3, etc.).

1. General Structure: System prompts include role definition layer, capability boundary layer, behavioral norms layer, output format layer, and safety guardrail layer.
2. Vendor Differences: OpenAI is pragmatic (concise balance between safety and usefulness), Anthropic is cautious and conservative (detailed safety instructions), Google integrates search advantages (emphasizes fact tracing), open-source models are diverse.
3. Evolution Trends: From static to dynamic, general to vertical, instructions to examples, single-language to multi-language.

1. Optimize Prompt Design: Avoid conflicts with underlying prompts, use existing behavior patterns to design precise supplementary instructions.
2. Safety and Compliance Assessment: Evaluate model risk exposure, supplement application-layer safety mechanisms.
3. Model Selection Reference: Choose Claude for strictly controlled scenarios, GPT-4 for creative scenarios, etc.

1. Information Disclosure Boundaries: Balance research transparency and trade secrets, avoid publishing content that directly leads to safety risks.
2. Legitimacy of Adversarial Research: Prompt injection is used to promote AI safety rather than malicious purposes; call for transparent disclosure mechanisms.
3. Interpretability: System prompt research helps understand the reasons behind model behavior and build human-AI trust.

Community Contributions: Welcome testing of new models, version tracking, analysis and interpretation, tool development, translation and organization. Future Directions: Analysis of multimodal model prompts, research on Agent system instruction architecture, analysis of prompt inheritance in fine-tuned models, exploration of prompt compression technology.