prompt-in-context-learning
Resources for in-context learning and prompt engineering with LLMs like ChatGPT and GPT-3
GraphCanon updated 3w · GitHub synced 3w
Decision brief
prompt-in-context-learning offers specialized resources for mastering large language models through advanced prompt engineering and in-context learning techniques.
Good fit when
- 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.
Avoid when
- 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.
- Requirements:
- Operates in Jupyter Notebook environments.
Observed Jul 17, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Steady (60d since push)
- As of 3w
- Provenance
- Not a fork · Personal account
- As of 3w
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/EgoAlpha/prompt-in-context-learningSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
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.
Capability facts
- Languages
- jupyter notebook
Source: github.language · Jul 28, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Jul 28, 2026)
🎉*: The method for quickly getting started with large language models by using LangChain.Source link
Source: README excerpt (regex_v1, Jul 28, 2026)
<a href="./chatgptprompt.md">🌍 ChatGPT Prompt</a> ｜Source link
Tags
README
An Open-Source Engineering Guide for Prompt-in-context-learning from EgoAlpha Lab.
📝 Papers | ⚡️ Playground | 🛠 Prompt Engineering | 🌍 ChatGPT Prompt ｜ ⛳ LLMs Usage Guide
⭐️ Shining ⭐️: 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.
The resources include:
🎉Papers🎉: The latest papers about In-Context Learning, Prompt Engineering, Agent, and Foundation Models.
🎉Playground🎉: Large language models（LLMs）that enable prompt experimentation.
🎉Prompt Engineering🎉: Prompt techniques for leveraging large language models.
🎉ChatGPT Prompt🎉: Prompt examples that can be applied in our work and daily lives.
🎉LLMs Usage Guide🎉: The method for quickly getting started with large language models by using LangChain.
In the future, there will likely be two types of people on Earth (perhaps even on Mars, but that's a question for Musk):
- Those who enhance their abilities through the use of AIGC;
- Those whose jobs are replaced by AI automation.
💎EgoAlpha: Hello! human👤, are you ready?
Table of Contents
- 🔥 AI Spotlight
- 📜 Papers
- Survey
- Prompt Engineering
- Prompt Design
- Chain of Thought
- In-context Learning
- Retrieval Augmented Generation
- Evaluation & Reliability
- Agent
- Multimodal Prompt
- Prompt Application
- Foundation Models
- 👨💻 LLM Usage
- ✉️ Contact
- 🙏 Acknowledgements
🔥 AI Spotlight: Trending Research Papers
[2026-05-29]
CubePart: An Open-Vocabulary Part-Controllable 3D Generator （New）
Published: 2026-05-27
Yiheng 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]
From Pixels to Words -- Towards Native One-Vision Models at Scale （New）
Published: 2026-05-27
Haiwen 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]
ESC-Skills: Discovering and Self-Evolving Skills for Emotional Support Conversations （New）
Published: 2026-05-27
Jie Zhu, Huaixia Dou, Shuo Jiang, Junhui Li, Lifan Guo, Feng Chen, Chi Zhang, Fang Kong - [arXiv]
Beyond Mode Collapse: Distribution Matching for Diverse Reasoning （New）
Published: 2026-05-19
Xiaozhe Li, Yang Li, Xinyu Fang, Shengyuan Ding, Peiji Li, Yongkang
For agents
This page has a .md twin and JSON over the API.