system-prompts-and-models-of-ai-tools

Ok, you can already get them all! :) Enjoy the read, have fun! https://github.com/x1xhlol/system-prompts-and-models-of-ai-t... https://github.com/asgeirtj/system_prompts_leaks https://github.com/elder-plinius/CL4R1T4S

This repo [1] is a brilliant illustration of the copium going into this. Third line of the Claude prompt [2]: IMPORTANT: You must NEVER generate or guess URLs for the user - Who knew solving LLM hallucinations was just that easy? IMPORTANT: DO NOT ADD ***ANY*** COMMENTS unless asked - Guess we need triple bold to make it pay attention now? It gets even more ludicrous when you see the recommendation that you should use a LLM to write this slop of a .cursorrules file for you. [1]: https://github.com/x1xhlol/system-prompts-and-models-of-ai-t... [2]: https://github.com/x1xhlol/system-prompts-and-models-of-ai-t...

you can - but they dont work reliably in practice. Common issues include search match fails, missing commas in replaced items (model doesnt have surround context while replacing), and a few other error cases. This issues are much worse for scattered edits across a file from real world queries (ex: make this page look nicer). Patches tend to work fine for single line or extremely focused edits though - Cursor uses s&r/patches for single line edits: https://github.com/x1xhlol/system-prompts-and-models-of-ai-t...

Maybe, but also, some of the most popular ai-assisted coding / vibecoding platforms are using system prompts that are 1.5k+ lines long[0] 0: https://github.com/x1xhlol/system-prompts-and-models-of-ai-t...

except the area is so hugely popular with people who unfortunately lack the rigor or curiosity to ask for this and blindly believe claims. for example this hugely popular repository https://github.com/x1xhlol/system-prompts-and-models-of-ai-t... where the authors fail to explain how the prompts are obtained and how they prove that they are valid and not a hallucination.

> 2.3 Specialized Micro-Agents Over Generalized Rules Initially, our instinct was to continuously add more rules into a single large prompt to handle edge cases This has been my experience as well. However, it seems like the platforms like Cursor/Lovable/v0/et al are doing things differently For example, this is Lovable’s leaked system prompt, 1550 lines: https://github.com/x1xhlol/system-prompts-and-models-of-ai-t... Is there a trick to making gigantic system prompts work well?

That’s also been my experience At the same time, I’ve seen the system prompts for a few agents (https://github.com/x1xhlol/system-prompts-and-models-of-ai-t...), and they are huge How does that work?

I believe tricking a system to reveal its system prompt is the new reverse engineering, and I've been wondering what techniques are used to extract this type of information? For instance, major AI-powered IDEs had their system prompts revealed and published publicly: https://github.com/x1xhlol/system-prompts-and-models-of-ai-t...