Here’s something I do all the time to write perfect prompts: I hunt down leaked/open-sourced system prompts and study them.

In this post, I’ll explain why this approach works and how you can do it too, giving you a shortcut to prompt engineering mastery.

Why study existing system prompts?

1. Learn from the original creators

Think about it - who better to learn from than the source? The companies that built the models know them best. OpenAI, Anthropic, and other AI developers have deep insights into how their models work internally.

By studying their system prompts, you can learn:

• How they define tool definitions
• What writing style works best with each model
• How they structure information for optimal processing

Every model has a different “vibe” - Claude responds differently than GPT-4o, which is different from Gemini. Learning from the source gives you model-specific knowledge that generic advice can’t provide.

2. Master the art of using separators

Speaking of model-specific tricks, let’s talk about separators. One fascinating insight I’ve gained from studying these prompts is how different models respond to different separator styles:

From what I’ve noticed, Claude works better with XML tags like: <context>Your content here</context>

While GPT models might prefer separator with a semicolon:

knowledge:

Good separators are crucial when you’re dumping tons of context into your prompts. They reduce hallucination and help the model organize information properly

3. Discover optimal prompt length strategies

But separators alone aren’t enough. Another critical factor is length management. LLMs get messy when context gets too long. Memory fades, instructions get forgotten, and performance degrades.

By studying leaked prompts from major companies, I’ve figured out:

• How to structure critical information so it stays “top of mind” for the model
• Optimal lengths for instructions in complex applications
• When to be verbose and when to be concise

TLDR: Keep it clear, straightforward, and simple. The best prompts are often shorter than you might expect - a counterintuitive insight I gained from professional prompt examples.

4. Save massive time and energy

This brings me to perhaps the most practical benefit: efficiency. Big companies have spent millions of dollars and countless hours testing their prompts. They’ve run thousands of iterations to find what works best.

Why reinvent the wheel? By remixing their proven approaches for your specific needs, you can skip months of trial and error and get straight to results.

Where to find these gold-standard prompts

Now that you understand the benefits, you’re probably wondering where to find these valuable resources. Ready to start your prompt collection? Here’s where to look:

1. Reddit communities - Subreddits like r/GPT4, r/ChatGPT, and r/LocalLLaMA often have users sharing discovered system prompts

2. GitHub repositories - Check out collections like:

   • jujumilk3/leaked-system-prompts
   • schroneko/systemprompts

   These repositories compile leaked system prompts from various sources

3. Official documentation - Interestingly, some companies like Anthropic openly share their system prompts. Check out Claude’s documentation for examples - a treasure trove of official insights.

Conclusion

It all comes full circle - the best prompt engineers aren’t necessarily the most creative - they’re the most observant. By studying what works for the companies that built these models, you can shortcut your way to prompt engineering mastery.

Next time you’re struggling to get the perfect output from an AI, don’t just experiment blindly. Find out how the experts do it, adapt their approaches to your needs, and watch your results improve dramatically.

Remember: good artists copy, great artists steal. The same applies to prompt engineering.