Leaked LLM Prompts Reference Guide

Key takeaways#

• leaked-llm-prompts is a GitHub resource collection for studying prompts that have been shared from LLM products and assistants.
• Treat the repository as research material, not as official documentation for any vendor.
• The useful workflow is comparison: look at structure, safety framing, tool instructions, output rules, and retrieval patterns across prompts.
• Do not copy private prompts into a production system without review. Use the collection to learn design patterns and failure modes.

What this resource is#

leaked-llm-prompts is a public GitHub repository described as a collection of leaked prompts. It is not an SDK, model, MCP server, or hosted application. The value is educational: prompt engineers, AI product builders, and security reviewers can inspect examples and compare how different assistants frame roles, instructions, refusal behavior, tool use, formatting, and hidden constraints.

Because the material is sourced from public GitHub content rather than vendor documentation, every example should be treated carefully. A prompt may be old, incomplete, modified, or taken out of context. Use it to understand prompt architecture and recurring product patterns, not as proof of a current system design.

How builders can use it#

Start by reading for structure rather than wording. Look for the order of instructions, where the prompt defines the assistant role, how it scopes tools, how it separates user content from system policy, and what output contracts it sets. Those patterns are more durable than any single phrase.

Next, compare examples across products. Strong prompts tend to make the model's job explicit, reduce ambiguity, and describe what to do when the user asks for something outside scope. Weaker prompts often mix policy, examples, and product copy in ways that can create conflicts. A prompt collection helps reviewers spot these patterns quickly.

Safety and legal notes#

Do not assume a prompt is licensed for reuse. Do not paste proprietary text into your own product. If you use the repo during a prompt review, write fresh instructions that match your product, policy, and data access model. Also test with red-team cases; copied prompt text rarely fits a different tool environment.

Practical review checklist#

1. Identify the assistant's role and target user.
2. Mark every tool instruction and data-access rule.
3. Check how the prompt handles uncertainty and refusal.
4. Separate style guidance from safety guidance.
5. Rewrite patterns in your own words before testing.