In the future, there will likely be two types of people on Earth (perhaps even on Mars, but that’s a question for Musk):
👉Complete paper list 🔗 for "Survey"👈
Prompt Engineering
Prompt Design
👉Complete paper list 🔗 for "Prompt Design"👈
Chain of Thought
👉Complete paper list 🔗 for "Chain of Thought"👈
In-context Learning
👉Complete paper list 🔗 for "In-context Learning"👈
Retrieval Augmented Generation
👉Complete paper list 🔗 for "Retrieval Augmented Generation"👈
Evaluation & Reliability
👉Complete paper list 🔗 for "Evaluation & Reliability"👈
Agent
👉Complete paper list 🔗 for "Agent"👈
Multimodal Prompt
👉Complete paper list 🔗 for "Multimodal Prompt"👈
Prompt Application
👉Complete paper list 🔗 for "Prompt Application"👈
Foundation Models
👉Complete paper list 🔗 for "Foundation Models"👈
👨💻 LLM Usage
Large language models (LLMs) are becoming a revolutionary technology that is shaping the development of our era. Developers can create applications that were previously only possible in our imaginations by building LLMs. However, using these LLMs often comes with certain technical barriers, and even at the introductory stage, people may be intimidated by cutting-edge technology: Do you have any questions like the following?
- ❓ How can LLM be built using programming?
- ❓ How can it be used and deployed in your own programs?
💡 If there was a tutorial that could be accessible to all audiences, not just computer science professionals, it would provide detailed and comprehensive guidance to quickly get started and operate in a short amount of time, ultimately achieving the goal of being able to use LLMs flexibly and creatively to build the programs they envision. And now, just for you: the most detailed and comprehensive Langchain beginner’s guide, sourced from the official langchain website but with further adjustments to the content, accompanied by the most detailed and annotated code examples, teaching code lines by line and sentence by sentence to all audiences.
Click 👉here👈 to take a quick tour of getting started with LLM.
This repo is maintained by EgoAlpha Lab. Questions and discussions are welcome via [email protected].
We are willing to engage in discussions with friends from the academic and industrial communities, and explore the latest developments in prompt engineering and in-context learning together.
🙏 Acknowledgements
Thanks to the PhD students from EgoAlpha Lab and other workers who participated in this repo. We will improve the project in the follow-up period and maintain this community well. We also would like to express our sincere gratitude to the authors of the relevant resources. Your efforts have broadened our horizons and enabled us to perceive a more wonderful world.
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