EgoAlpha/prompt-in-context-learning: A Curated LLM Paper and Guide Repository

What this repository collects and who uses it

The repository describes itself as an open-source engineering guide for Prompt-in-context-learning from EgoAlpha Lab. In practice it is an awesome-list style collection combined with standalone Markdown guides.

The primary audience is ML researchers and engineers who want to track recent papers on in-context learning (ICL), understand prompting techniques, or find practical examples for working with LLMs in production. The repository is organized around a recurring tension in the field: in-context learning as a research topic (how models generalize from examples in the prompt) and prompt engineering as a practitioner skill (how to write prompts that produce useful outputs).

The README describes the intended reader as someone preparing to work in an environment where AI tools are central, though that framing is aspirational context rather than a usage constraint. The repository itself is neutral and works for anyone reading research papers or following LLM technique development.

Repository structure: five main components

The repository's top-level entries reveal five components that a reader should navigate separately:

1. README.md: the AI Spotlight section (recent paper highlights) and the main paper list, organized by category. This is the primary entry point and the most frequently updated part of the repository.

2. PaperList/: a directory of Markdown files that contain the full paper lists by category, linked from the README. Individual category files include survey.md and topic-specific lists for chain-of-thought, in-context learning, RAG, and agents.

3. PromptEngineering.md: a standalone guide on prompting techniques for LLMs. This is the practical engineering reference.

4. chatgptprompt.md: a collection of ready-to-use prompts for work and daily tasks, along with a Chinese version at chatgptprompt_zh.md.

5. langchain_guide/: a directory containing LangChain tutorials. The main file is LangChainTutorial.ipynb, a Jupyter notebook covering how to get started with LLMs using LangChain.

The historynews.md file archives past AI Spotlight entries, providing a timeline of papers added to the repository.

To use the repository locally, clone it and open the files directly:

git clone https://github.com/EgoAlpha/prompt-in-context-learning.git
cd prompt-in-context-learning

After cloning, the Markdown files are readable in any text editor. The LangChain tutorial notebook at langchain_guide/LangChainTutorial.ipynb requires Jupyter to run. For browsing, GitHub renders all the Markdown files in place.

Paper categories and what each covers

The Papers section in the README is divided into eight categories:

- Survey: broad review papers on LLM capabilities and applications - Prompt Engineering: papers on prompt design and optimization methods - Chain of Thought: research on step-by-step reasoning in prompts - In-Context Learning: papers on how models learn from examples given in the prompt - Retrieval Augmented Generation: papers on combining retrieval with generation - Evaluation and Reliability: papers on measuring and improving LLM output quality - Agent: papers on LLM-based autonomous agents and multi-agent systems - Multimodal Prompt: papers on prompting across text, image, and other modalities - Prompt Application: applied papers on specific use cases - Foundation Models: papers on large-scale model training and architecture

Each category links to a fuller list in PaperList/. The README shows a curated sample of recent papers in each section before directing to the complete file. Papers are listed with their title, arXiv or DOI link, and publication date, with some entries linking to associated GitHub repositories.

The AI Spotlight section at the top of the README lists papers the maintainers consider particularly noteworthy from recent updates. The most recent Spotlight entry in the README is dated 2026-05-29 and includes papers on 3D generation, vision-language models, emotional support conversation, reasoning diversity, and KV cache quantization.

The LangChain usage guide and Playground

The LangChain tutorial is in langchain_guide/LangChainTutorial.ipynb, a Jupyter notebook that the README links as the LLMs Usage Guide. The primary language of the repository is listed as Jupyter Notebook, which reflects this component.

The older LangChain v1.0 tutorial directory is also present at the top level as `langchain v1.0教程/`, indicating the guide has been updated in place rather than versioned separately. Users who want the current guide should use the langchain_guide/ directory rather than the older one.

The Playground.md file lists LLM environments that support prompt experimentation. The README describes it as listing LLMs that enable prompt experimentation, though the README does not list the specific tools in its opening section. This file is separate from the paper list and serves as a reference for hands-on practice rather than reading.

The PromptEngineering.md guide covers prompting techniques for working with LLMs. The original version is preserved as PromptEngineering_orinignal.md, and a Chinese version exists at promptengineering_zh.md.

Limitations as a research resource

The repository's paper list is updated by the maintainers rather than through automated feeds from arXiv or semantic scholar. This means coverage depends on the maintainers' availability and interests. The most recent AI Spotlight entry is dated 2026-05-29, and the repository's last push was on 2026-05-29, which suggests active curation was happening up to that date.

The paper listings are links only. There are no abstracts, no citation counts embedded in the repository, and no search functionality beyond what GitHub's search provides across Markdown files. A reader who needs to filter papers by method type or find all papers that cite a specific work needs to go directly to arXiv or a citation index like Semantic Scholar.

The list covers a broad range of topics but does not claim to be exhaustive. Entire research directions may be underrepresented depending on when they emerged relative to the repository's last update cycle. Fast-moving areas like agent memory or multimodal reasoning can accumulate months of relevant papers before a batch update brings them in.

The repository does not cover fine-tuning, reinforcement learning from human feedback (RLHF), or model training directly. Those areas are adjacent but out of scope.

Comparison with learnprompting.org

learnprompting.org is an open-source community-maintained guide to prompt engineering. It is structured as a tutorial course with numbered chapters, exercises, and explanations aimed at practitioners who want to learn prompting from first principles. The content is prose-based and pedagogical.

EgoAlpha/prompt-in-context-learning takes a different approach: it is primarily a paper-tracking repository with supplementary guides, not a tutorial course. The LangChain notebook gives hands-on exposure to one library, but the core value is the curated paper list organized by research topic. learnprompting.org is better suited for someone learning to write prompts. EgoAlpha's repository is better suited for someone tracking the academic literature on in-context learning and related LLM topics.

Maintenance and license

The repository is not archived. The last push was on 2026-05-29. There are no GitHub releases. The project is tracked through commits to the main branch.

The license is MIT. The repository's content consists primarily of curated links to research papers and original Markdown guides. The MIT license applies to the repository files. Individual papers linked from the repository are published under their own terms (typically arXiv's open access policy or conference proceedings terms) and not under MIT.

Editorial conclusion

EgoAlpha/prompt-in-context-learning suits researchers and engineers who want a single entry point into LLM literature across in-context learning, prompt engineering, chain-of-thought, RAG, and agent techniques, along with practical LangChain tutorials. It is not useful if you need a searchable database, a structured citation index, or a tutorial series with worked exercises rather than a collection of paper links and Markdown guides. Before using it as a comprehensive literature source, check the date of the most recent AI Spotlight entry and compare it against arxiv.org directly, since the repository's update cadence determines how current the paper list is relative to fast-moving subfields.

Frequently asked questions

Official sources

1. EgoAlpha/prompt-in-context-learning on GitHub
2. Issues
3. License: MIT
4. Project website
5. README