{"data":{"slug":"egoalpha-prompt-in-context-learning","name":"prompt-in-context-learning","tagline":"Resources for in-context learning and prompt engineering with LLMs like ChatGPT and GPT-3","github_url":"https://github.com/EgoAlpha/prompt-in-context-learning","owner":"EgoAlpha","repo":"prompt-in-context-learning","owner_avatar_url":"https://avatars.githubusercontent.com/u/102293459?v=4","primary_language":"Jupyter Notebook","stars":2247,"forks":189,"topics":["ai-agent","chain-of-thought","chatbot","chatgpt","chatgpt-api","cot","in-context-learning","language-modeling","large-language-model","llm","pre-training","prompt","prompt-based-learning","prompt-engineering","prompt-learning"],"archived":false,"github_pushed_at":"2026-05-29T01:00:03+00:00","maintenance_label":"Steady","url":"https://www.graphcanon.com/tools/egoalpha-prompt-in-context-learning","markdown_url":"https://www.graphcanon.com/tools/egoalpha-prompt-in-context-learning.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/egoalpha-prompt-in-context-learning","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=egoalpha-prompt-in-context-learning","description":"Awesome resources for in-context learning and prompt engineering: Mastery of the LLMs such as ChatGPT, GPT-3, and FlanT5, with up-to-date and cutting-edge updates. ","homepage_url":"https://egoalpha.com","license":"MIT","open_issues":6,"watchers":41,"ai_summary":"Provides resources and tools focused on mastery of large language models through contemporary prompt engineering techniques including in-context learning, aimed at enhancing the capabilities of AI agents.","readme_excerpt":"
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\n\n**An Open-Source Engineering Guide for Prompt-in-context-learning from EgoAlpha Lab.**\n\n\n\n\n\n\n\n

\n\n ๐Ÿ“ Papers |\n โšก๏ธ Playground |\n ๐Ÿ›  Prompt Engineering |\n ๐ŸŒ ChatGPT Prompt ๏ฝœ\n โ›ณ LLMs Usage Guide \n\n

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\n\n> **โญ๏ธ Shining โญ๏ธ:** This is fresh, daily-updated resources for in-context learning and prompt engineering. As Artificial General Intelligence (AGI) is approaching, let's take action and become a super learner so as to position ourselves at the forefront of this exciting era and strive for personal and professional greatness.\n\nThe resources include:\n\n*๐ŸŽ‰[Papers](#๐Ÿ“œ-papers)๐ŸŽ‰*: The latest papers about *In-Context Learning*, *Prompt Engineering*, *Agent*, and *Foundation Models*. \n\n*๐ŸŽ‰[Playground](./Playground.md)๐ŸŽ‰*: Large language models๏ผˆLLMs๏ผ‰that enable prompt experimentation. \n\n*๐ŸŽ‰[Prompt Engineering](./PromptEngineering.md)๐ŸŽ‰*: Prompt techniques for leveraging large language models. \n\n*๐ŸŽ‰[ChatGPT Prompt](./chatgptprompt.md)๐ŸŽ‰*: Prompt examples that can be applied in our work and daily lives. \n\n*๐ŸŽ‰[LLMs Usage Guide](./chatgptprompt.md)๐ŸŽ‰*: The method for quickly getting started with large language models by using LangChain.\n\nIn the future, there will likely be two types of people on Earth (perhaps even on Mars, but that's a question for Musk): \n- Those who enhance their abilities through the use of AIGC; \n- Those whose jobs are replaced by AI automation.\n\n```\n\n๐Ÿ’ŽEgoAlpha: Hello! human๐Ÿ‘ค, are you ready?\n\n``` \n\n\n\n# Table of Contents\n- [๐Ÿ”ฅ AI Spotlight](#-ai-spotlight-trending-research-papers)\n- [๐Ÿ“œ Papers](#-papers)\n - [Survey](#survey)\n - [Prompt Engineering](#prompt-engineering)\n - [Prompt Design](#prompt-design)\n - [Chain of Thought](#chain-of-thought)\n - [In-context Learning](#in-context-learning)\n - [Retrieval Augmented Generation](#retrieval-augmented-generation)\n - [Evaluation \\& Reliability](#evaluation--reliability)\n - [Agent](#agent)\n - [Multimodal Prompt](#multimodal-prompt)\n - [Prompt Application](#prompt-application)\n - [Foundation Models](#foundation-models)\n- [๐Ÿ‘จโ€๐Ÿ’ป LLM Usage](#-llm-usage)\n- [โœ‰๏ธ Contact](#๏ธ-contact)\n- [๐Ÿ™ Acknowledgements](#-acknowledgements)\n\n\n\n# ๐Ÿ”ฅ AI Spotlight: Trending Research Papers\n\n\n\n\n\n### **[2026-05-29]**\n\n[**CubePart: An Open-Vocabulary Part-Controllable 3D Generator**](https://huggingface.co/papers/2605.28763) ๏ผˆ**New**๏ผ‰\n\n*Published: 2026-05-27*\n\nYiheng Zhu, Kangle Deng, Jean-Philippe Fauconnier, Inaki Navarro, Daiqing Li, Ava Pun, Yinan Zhang, Peiye Zhuang, Xiaoxia Sun, Maneesh Agrawala, Kiran Bhat, Tinghui Zhou - [arXiv]\n\n\n\n---\n\n\n[**From Pixels to Words -- Towards Native One-Vision Models at Scale**](https://huggingface.co/papers/2605.28820) ๏ผˆ**New**๏ผ‰\n\n*Published: 2026-05-27*\n\nHaiwen Diao, Jiahao Wang, Penghao Wu, Yuhao Dong, Yuwei Niu, Yue Zhu, Zhongang Cai, Weichen Fan, Linjun Dai, Silei Wu, Xuanyu Zheng, Mingxuan Li, Yuanhan Zhang, Bo Li, Hanming Deng, Huchuan Lu, Quan W - [arXiv]\n\n\n\n---\n\n\n[**ESC-Skills: Discovering and Self-Evolving Skills for Emotional Support Conversations**](https://huggingface.co/papers/2605.27908) ๏ผˆ**New**๏ผ‰\n\n*Published: 2026-05-27*\n\nJie Zhu, Huaixia Dou, Shuo Jiang, Junhui Li, Lifan Guo, Feng Chen, Chi Zhang, Fang Kong - [arXiv]\n\n\n\n---\n\n\n[**Beyond Mode Collapse: Distribution Matching for Diverse Reasoning**](https://huggingface.co/papers/2605.19461) ๏ผˆ**New**๏ผ‰\n\n*Published: 2026-05-19*\n\nXiaozhe Li, Yang Li, Xinyu Fang, Shengyuan Ding, Peiji Li, Yongkang","github_created_at":"2023-03-08T08:47:29+00:00","created_at":"2026-07-11T11:59:00.723727+00:00","updated_at":"2026-07-28T12:00:20.301869+00:00","categories":[{"slug":"ai-agents","name":"AI Agents","url":"https://www.graphcanon.com/categories/ai-agents","markdown_url":"https://www.graphcanon.com/categories/ai-agents.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/ai-agents"},{"slug":"llm-frameworks","name":"LLM Frameworks","url":"https://www.graphcanon.com/categories/llm-frameworks","markdown_url":"https://www.graphcanon.com/categories/llm-frameworks.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/llm-frameworks"}],"tags":[{"slug":"ai-agent","name":"ai-agent"},{"slug":"chain-of-thought","name":"chain-of-thought"},{"slug":"chatbot","name":"chatbot"},{"slug":"in-context-learning","name":"in-context-learning"},{"slug":"large-language-model","name":"large-language-model"},{"slug":"prompt-engineering","name":"prompt-engineering"}],"trust":{"provenance":{"is_fork":false,"github_id":611161753,"owner_type":"User","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-07-28T12:00:19.366Z","maintenance":{"label":"Steady","score":60,"methodology":"github_public_v1","releases_90d":0,"days_since_push":60,"last_release_at":null},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T11:59:02.098Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-07-28T12:00:20.015Z"},"languages":{"value":["jupyter notebook"],"source":"github.language","observed_at":"2026-07-28T12:00:20.015Z"},"license_spdx":{"value":"MIT","source":"github.license","observed_at":"2026-07-28T12:00:20.015Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":{"notes":["Operates in Jupyter Notebook environments."]},"constraints":null,"when_to_use":["Use when seeking to enhance the capabilities of AI agents specifically using cutting-edge prompt engineering techniques such as those used with ChatGPT, GPT-3, or FlanT5.","Ideal if your focus is on up-to-date methodologies for optimizing language model performance in real-world applications."],"when_not_to_use":["Not recommended if you require functionalities specific to other AI frameworks that do not align with the prompt engineering techniques focused on here.","Avoid this resource if your project strictly focuses on areas outside of in-context learning and advanced LLMs like ChatGPT or GPT-3."],"source":"enrich:decision_facts","observed_at":"2026-07-17T00:04:02.970Z"},"constraint_facets":null,"decision_summary":[{"label":"Requirements","value":"Operates in Jupyter Notebook environments."},{"label":"Adopt for","value":"prompt-in-context-learning offers specialized resources for mastering large language models through advanced prompt engineering and in-context learning techniques."},{"label":"License detail","value":"The tool is open-source under the MIT license, allowing for free use, modification, and distribution with certain conditions."}]}}