prompt in context learning

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Basic Information

This repository is an open-source engineering guide and continuously updated resource hub for prompt-in-context-learning and prompt engineering maintained by EgoAlpha Lab. It aggregates recent research papers, trending AI spotlight items, practical guides and curated lists focused on in-context learning, prompt design, chain-of-thought, retrieval augmented generation, agent research, multimodal prompting and foundation models. The repo provides hands-on materials such as a Playground for prompt experimentation, a LangChain beginner tutorial notebook, a prompt engineering reference, and a collection of ChatGPT-ready prompt examples. The stated goal is to help practitioners, students and researchers learn prompt techniques, track new literature and apply LLMs effectively. The content is organized into topic sections and PaperList directories, updated frequently, and accompanied by history news and acknowledgements. The repository emphasizes accessibility with annotated code examples and step-by-step guidance to lower barriers to building and using LLM-based applications.

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Features
Curated, regularly updated collections of research and engineering materials covering surveys, prompt engineering subtopics, chain-of-thought, in-context learning, RAG, evaluation and agent papers. Dedicated pages and files include Playground.md for experimentation, PromptEngineering.md for techniques, chatgptprompt.md with ready prompts, and a LangChainTutorial.ipynb notebook with annotated, beginner-friendly code. The repo organizes papers into PaperList folders and a historynews log for chronological updates. It highlights an AI Spotlight section for trending papers and provides categorized tables of contents for quick navigation. Metadata and badges indicate versioning and community recognition. Contact information and acknowledgements are included to encourage discussion and contributions. The structure favors researchers and practitioners seeking literature, examples, and applied guidance rather than a software framework or runtime library.
Use Cases
This repository helps researchers, engineers and learners discover and apply the latest ideas in prompt design and in-context learning by centralizing curated papers, practical guides and example prompts. Beginners benefit from the LangChain tutorial notebook that walks through LLM usage with annotated code, while practitioners can use the Playground and ChatGPT prompt examples to prototype and iterate quickly. The categorized paper lists and AI Spotlight make it easier to follow emerging trends and find relevant surveys or implementations. History news and frequent updates reduce the time needed to keep current with the literature. The contact details enable community discussion and feedback. Overall, the repo serves as a reference and learning aid for improving prompt technique, experimenting with LLMs, and selecting research to read or reproduce.

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