repository·main·Indexed 27 days ago
https://github.com/promptslab/awesome-prompt-engineeringA curated repository of resources, research, and tools focused on Prompt Engineering and Context Engineering for Large Language Models (LLMs). It includes a learning path for beginners, surveys on prompting techniques, research on prompt optimization and compression, reasoning advances, agentic prompting, security and red-teaming tools, and a comprehensive list of prompt management, evaluation, and agent frameworks.
This section of the Awesome Prompt Engineering repository provides a curated list of tools, frameworks, and research papers focused on autonomous research and self-improving agents. It is categorized into several functional groups:
recursive-improve, auto-research, gemini-autoresearch).AI-Scientist, AutoResearch-SibylSystem, AgentLaboratory).MLAgentBench, mle-bench, AgentBench).Engage with the community through the following channels:
website/. No additional configuration is required.For implementation-specific best practices, refer to the official documentation provided by major AI model providers:
To run the website locally, navigate to the website directory, install the dependencies, and start the development server.
cd website
npm install
npm run devIf you are new to prompt engineering, follow this recommended learning path:
The repository provides a curated list of comprehensive surveys covering various aspects of prompt engineering, including taxonomies of techniques, efficiency-oriented methods, and reasoning topologies. Key surveys include:
Use these independent and community-driven resources for broad techniques and learning paths:
You can contribute to this repository by adding new resources or updating existing ones. Before submitting a contribution, review the contribution guidelines.
Accepted contributions include:
Quality Standards:
A collection of tools for managing, testing, and evaluating LLM prompts. Key tools include:
.cursorrules, CLAUDE.md).Meta's Llama models are available via 25+ cloud partners, Hugging Face, and inference APIs. They include large-scale Mixture-of-Experts (MoE) architectures and dense models.
| Model | Architecture | Context | Key Feature |
|:------|:------------|:--------|:------------|
| Llama 4 Scout | 109B MoE / 17B active | 10M | Fits single H100, multimodal, open-weight |
| Llama 4 Maverick | 400B MoE / 17B active, 128 experts | 1M | Beats GPT-4o, open-weight |
| Llama 3.3 70B | Dense | 128K | Matches Llama 3.1 405B |