7000+ Lines of AI Tool System Prompts: Deep Dive into an Open-Source Project with Practical Guide

Project Overview: An Open-Source Treasure Trove of 7000+ Lines of System Prompts

In AI application development, the system prompt is the core element that determines how an AI tool behaves. GitHub user Kshiteej006 recently open-sourced a project called system-prompts-and-models-of-ai-tools, which compiles over 7,000 lines of system prompts and model information from various AI tools, providing developers and researchers with an invaluable prompt engineering resource.

The project currently has 41 stars, but its practical value and systematic approach make it well worth your attention. It's more than just a collection of prompts — it's a window into the design philosophies behind mainstream AI products like ChatGPT, Claude, and Gemini.

Why Do System Prompts Matter?

Defining an AI's "Personality" and "Capability Boundaries"

A system prompt is a set of hidden instructions preset before every conversation in an AI application. It determines the AI assistant's role, response style, capability scope, and safety boundaries. The distinctly different "personalities" exhibited by ChatGPT, Claude, Gemini, and other products are largely a result of their carefully crafted system prompts.

From a technical standpoint, system prompts are passed to the large language model via the system role message during API calls. In OpenAI's Chat Completions API, messages are divided into three roles: system, user, and assistant. The system message carries the highest instruction priority — the model treats it as an inviolable foundational directive. This mechanism stems from the instruction tuning and RLHF (Reinforcement Learning from Human Feedback) training processes, where models are trained to prioritize system-level instructions. Different models vary in how strictly they adhere to system prompts, which is the technical root cause behind why the same prompt can produce different results across models.

For developers, studying the system prompts of mature products offers a fast track to understanding industry best practices — how to define roles, handle edge cases, and balance helpfulness with safety. These are critical topics that any AI application builder must address.

Essential Reference Material for Prompt Engineering

Prompt Engineering has become a core skill in the AI era. The field has evolved from early experiential tricks into a systematic engineering discipline. Since 2023, the academic community has proposed multiple structured methodologies, including reasoning-enhancement techniques like Chain-of-Thought (CoT), Tree-of-Thought (ToT), and Self-Consistency, as well as practical paradigms such as Few-shot Learning and Retrieval-Augmented Generation (RAG). At the enterprise level, "Prompt Engineer" has become a standalone job title, with salaries in Silicon Valley ranging from $150K to $300K per year. The quality of system prompt design directly determines an AI product's user experience and commercial value.

By analyzing system prompts from different tools, developers can learn:

Structured instruction design: How to organize complex, multi-layered instructions
Constraint setting: How to effectively limit the model's output scope
Role-playing frameworks: How to get AI to accurately assume a specific persona
Safety guardrail design: How to prevent malicious exploitation through prompt injection and other techniques

Deep Dive into the Project's Content

Comprehensive Coverage of Major AI Tools

The project includes system prompts from a wide range of mainstream AI tools, covering the current popular AI product ecosystem. With over 7,000 lines of content, this is far from a superficial snippet collection — it provides fairly complete records of each tool's system prompts. This offers a rare opportunity for side-by-side comparison of different AI products' design philosophies.

Developers can directly observe how different products handle:

Conversation openers and role definitions
Sensitive topic avoidance and redirection mechanisms
Context management strategies in multi-turn conversations
Instruction formats for Function Calling

Function Calling is a critical capability enabling large language models to interact with external systems. It works by defining JSON Schema descriptions of available tools (including function names, parameter types, and usage descriptions) within the system prompt. During inference, the model determines whether a tool call is needed and generates a structured invocation request. OpenAI first introduced this feature in June 2023, followed by Anthropic's Claude and Google's Gemini. Different products use notably different formats and strategies for describing tools in system prompts, which directly impacts the accuracy and reliability of tool invocations. Through this project, developers can intuitively compare each product's tool description paradigms.

The Relationship Between Models and Prompt Compatibility

Beyond the system prompts themselves, the project also documents information about the associated AI models. This is especially practical — the same prompt can produce vastly different results on GPT-4, Claude 3, Gemini Pro, and other models. Understanding the compatibility relationship between models and prompts is crucial for real-world AI application development.

The reason different large language models respond differently to the same prompt lies in their distinct training data distributions, alignment strategies, and architectural differences. GPT-4 employs large-scale RLHF training, giving it strong complex instruction-following capabilities. The Claude series uses Constitutional AI for alignment, excelling in safety but sometimes over-refusing. Gemini deeply integrates Google's multimodal capabilities. Additionally, each model's context window size (ranging from 8K to 200K tokens) affects system prompt design strategies — longer contexts allow for more detailed instructions but may also lead to "Lost in the Middle" attention degradation, where the model pays less attention to information in the middle of long texts. When designing system prompts, developers need to adjust instruction length, structure, and placement of key information based on the target model's characteristics.

Practical Use Cases

Value for AI Application Developers

Rapid prototyping: When building new AI applications, referencing proven prompt structures can significantly shorten iteration cycles
Security reference: Learn how leading products design safety guardrails to effectively prevent prompt injection attacks
Prompt performance optimization: Identify more efficient prompt formulations through comparative analysis of different products
Multi-scenario adaptation: Understand system prompt design patterns for different scenarios, such as customer service, writing assistants, coding assistants, etc.

Regarding security, Prompt Injection is one of the most closely watched attack vectors in AI safety. Attackers craft malicious user inputs in an attempt to override or bypass the safety constraints set in system prompts. Common techniques include role-play induction (e.g., "ignore all previous instructions"), indirect injection (planting malicious instructions through external data sources), and progressive jailbreaking across multi-turn conversations. OWASP has listed prompt injection as the #1 security risk for large language model applications. Studying the safety guardrail designs of mature products reveals how the industry mitigates this threat through layered defenses, input filtering, and output detection. The safety-related prompt snippets from various products collected in this project provide developers with invaluable defensive design references.

Value for AI Researchers

This collection provides precious first-hand material for studying AI product design patterns. Researchers can analyze industry trends — such as commonalities and differences in how products handle sensitive topics, multilingual support, and tool calling — to support academic papers and industry reports with concrete data.

Usage Tips and Considerations

Understand the Design Intent — Don't Just Copy

System prompt design must be tightly coupled with the specific application scenario, target users, and underlying model. Directly copying another product's prompts rarely yields good results. The right approach is to understand the underlying design intent, extract generalizable design patterns, and then customize based on your own requirements.

Be Mindful of Copyright and Compliance Risks

Some AI tools' system prompts may involve trade secrets or intellectual property concerns. When using these resources, it's advisable to limit usage to learning and research purposes. Avoid directly replicating others' prompt designs in commercial products to mitigate potential legal risks.

Keep Up with Project Updates

AI tools' system prompts are continuously adjusted and optimized as products iterate. As an open-source resource, this project's long-term value depends on sustained community contributions. The project currently has 6 forks, and we look forward to more prompt engineering enthusiasts joining in to maintain and expand this valuable resource.

Conclusion

Although the system-prompts-and-models-of-ai-tools project is modest in scale, it fills an important information gap — providing the AI development community with a centralized open-source platform for viewing and comparing system prompts across various AI tools. As prompt engineering becomes increasingly important, the value of resources like this will only continue to grow.

Whether you're an AI application developer, a prompt engineer, or an AI researcher, this project deserves a place in your toolbox as an essential reference for everyday development and learning.