Yesterday, a community prompt library crossed 888 GitHub stars. It’s called ai-setup, and the twist is what’s actually inside: not generic “be helpful” boilerplate, but production-tested templates optimized per model, separate versions for GPT-4, Claude 3.5, and Gemini 2.5 Pro.
Most prompt libraries treat all models like they’re interchangeable. This one doesn’t.
What’s in the repo
The collection covers the patterns that show up in real AI builds:
- Chain-of-thought and structured output templates for complex reasoning
- Function calling and tool-use prompt schemas
- RAG query patterns
- Multi-step agent instruction prompts
- Few-shot and zero-shot examples
Nearly 100 forks already, which means people aren’t just starring it, they’re building with it.
The twist worth paying attention to
Each template is model-specific. A chain-of-thought prompt tuned for Claude 3.5 handles multi-step reasoning differently than the GPT-4 version. One community member noted the Claude templates hold context unusually well across complex tasks, which matches what a lot of builders have figured out independently but never written down anywhere. Now it’s written down.
How to actually use 🔍
- Browse by task type: reasoning, RAG, tool-use, or agents
- Pick the template that matches your model
- Drop it into your system prompt as-is first, don’t edit yet
- Run your task and note exactly where it breaks
- 🛠️ Now customize, you’re editing from a working baseline, not guessing from scratch
Pro tip
Don’t just copy the templates. Study how they’re structured. The RAG query patterns in particular show a formatting logic that transfers to prompts the repo doesn’t cover yet. Reading the template teaches you more than using it.
Worth contributing to 🤝
888 stars is enough signal that there’s a real community forming here. If you’ve got a system prompt that works in production, especially for edge cases like multi-agent handoffs or structured output validation, open a PR. The library only gets useful when practitioners add the stuff that isn’t in any tutorial.
👉 github.com/caliber-ai-org/ai-setup
Frequently Asked Questions
Q: Where can I find the RAG query templates?
The RAG query templates are included in the repository and organized by use case. Start with the repo’s README or main directory structure, RAG, agents, and function calling templates are grouped together for easy discovery. If you need help locating them, the GitHub discussions are a good place to ask.
Q: How do these prompts maintain context in long, multi-step workflows?
Users specifically praise how well these templates handle multi-step tasks without losing context. The chain-of-thought and structured patterns preserve thread continuity across complex agent workflows, something that’s notoriously tricky to get right in generic templates but baked into these production-tested examples.
Q: Can I use these templates as-is, or do I need to customize them?
They’re designed as battle-tested starting points you can use immediately. Many developers report good results without modification, but you can also customize for your models, domain, or workflow. The repo includes model-specific versions (GPT-4, Claude 3.5, Gemini 2.5 Pro) to reduce customization work.
Q: How do I contribute my own battle-tested prompts?
The repo welcomes community contributions via GitHub PRs. Share your production-tested prompts, and the maintainers will help integrate them. It’s a collaborative effort to build a living library of real-world prompts that actually work.
Open-source collection of battle-tested system prompt templates just hit 888 stars — contribute yours
by u/Substantial-Cost-429 in PromptEngineering