EgoAlpha Prompt In-Context Learning Guide
An open-source engineering guide to prompt-in-context learning, featuring latest papers, prompt examples, and a LangChain tutorial.
- Source repo
- EgoAlpha/prompt-in-context-learning
- Stars
- ★ 2.2k
- Last updated
- 4mo ago
- License
- MIT
- Primary language
- Jupyter Notebook
- FA score
- 23/100 · Major gaps
At a glance
- How it runs
- Works with
- Universal · cross-platform
- Cost
- Free, no paid service needed
- Setup effort
- Medium · a few setup steps
- You'll need
- Typical use
- Researchers: Quickly find the latest papers on specific topics like chain-of-thought or agents, tracking cutting-edge developments.
- Not a fit if
- Developers wanting runnable agent code out of the box
- Researchers needing peer-reviewed sources to cite
- Learners unwilling to set up Python dependencies themselves
- Source review
- 23/100 · Major gaps 8 safety controls not found
What does this agent do, and when should you use it?
This is an open-source resource repository maintained by EgoAlpha Lab, focused on prompt engineering and in-context learning. It aggregates the latest research papers, ready-to-use prompt examples, a list of LLM playgrounds, and a beginner-friendly LangChain tutorial. The content is updated daily, making it a valuable asset for researchers and developers to stay current with LLM advancements. The repository is organized into Markdown files, including paper lists, prompt examples, and a tutorial notebook, facilitating easy browsing and learning.
The repository provides: 1) A categorized list of recent papers on topics like prompt design, chain-of-thought, in-context learning, RAG, agents, and multimodal prompting, each with links and citation info. 2) Practical ChatGPT prompt examples for work and daily life. 3) A list of LLM platforms that support prompt experimentation. 4) A detailed LangChain tutorial (Jupyter notebook) teaching how to build and deploy LLM applications with LangChain. Users can browse the Markdown files or run the notebook to learn.
- Researchers: Quickly find the latest papers on specific topics like chain-of-thought or agents, tracking cutting-edge developments.
- Developers: Reference ChatGPT prompt examples to design effective prompts for their applications.
- Beginners: Learn step-by-step how to build LLM applications through the LangChain tutorial.
- Educators: Use the resources as teaching materials to introduce prompt engineering and in-context learning concepts.
- Prompt engineers: Discover prompt templates and optimization tips for various scenarios.
How do you install or deploy this agent?
This is a pure resource collection with no installation required. Simply clone the repository or browse it directly on GitHub. To clone: git clone https://github.com/EgoAlpha/prompt-in-context-learning.git.
How do you use this agent?
- After cloning, open the Markdown files with any reader (e.g., VS Code). 2. Check
README.mdfor navigation, and use thePaperListfolder for complete paper lists. 3. Refer tochatgptprompt.mdfor prompt examples. 4. To run the LangChain tutorial, openlangchain_guide/LangChainTutorial.ipynbin Jupyter Notebook and install the required Python dependencies as needed.
What are this agent's strengths and limitations?
- Daily updates with the latest research papers.
- Well-organized content by topic, making navigation easy.
- Provides ready-to-use ChatGPT prompt examples.
- Includes a practical LangChain tutorial for beginners.
- Licensed under MIT, free to use.
- Primarily a resource list, not runnable agent code.
- Papers are newly published and may not be peer-reviewed.
- LangChain tutorial requires manual dependency and environment setup.
- Update frequency may depend on maintainer availability.
How does this agent compare with similar options?
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| EgoAlpha Prompt In-Context Learning Guide This agent | 23 · Major gaps | Library / SDKFree | ★ 2.2k | 4mo ago | Jupyter Notebook | — |
| Controllable Complex RAG | 40 · Major gaps | Library / SDKFree + model costs | ★ 1.6k | 27d ago | Jupyter Notebook | OpenAI API |
| qKnow Enterprise Knowledge Platform | 38 · Major gaps | Self-hosted serviceFreemium | ★ 285 | 3d ago | Java | — |
| Prompt Engineering Guide | 28 · Major gaps | Web appFree | ★ 79k | 7mo ago | MDX | ChatGPT · OpenAI API |
How does FollowAgents rate this agent?
Why each dimension lost points
Evidence: The repository is a resource list with no code execution, permission requests, or data processing logic, so all trust criteria lack evidence and score 0. No malicious behavior found, but no security mechanisms either.
Evidence: README is well-structured but has internal link inconsistencies (e.g., LLMs Usage Guide points to chatgptprompt.md instead of LangChain tutorial), and no dependency management or error handling documentation, so self-consistency scores 1, others 0.
Evidence: Clearly targets researchers and developers, offers multiple usage scenarios (papers, prompt engineering, ChatGPT prompts), but does not define capability boundaries or trigger conditions, environment fit only mentions LangChain, so scores are 2,1,0,1.
Evidence: Clear table of contents and categorization, provides examples and FAQ (e.g., ChatGPT prompts), MIT license explicit, version v3.0.0 but no changelog, maintenance responsibility unclear, so scores are 2,0,1,2,0,2,1,1.
Evidence: Output is a resource list, directly usable, but lacks practical application examples or cost-benefit analysis, so scores are 1,2,1.
Evidence: Paper links are traceable, but no cross-validation or separation of facts and inferences, so scores are 1,1,0.
- Not found in source: least-privilege scopingGrant only what the task needs: a dedicated account or read-only token, scoped to specific directories and repos.
- Not found in source: confirmation before actingTurn on (or add) a confirmation step before it acts, and try it in a sandbox or test environment before real data.
- Not found in source: data-flow disclosureWatch which external services it contacts (proxy or firewall logs) and keep sensitive data out until you know where it goes.
- Not found in source: sensitive-data handlingUse dedicated, low-privilege, revocable API keys — never production credentials — and keep secrets out of logs.
- Not found in source: dependency securityPin versions and run a dependency audit (npm audit, pip-audit) before installing; prefer running it in a container.
- Not found in source: disclosed external effectsEstablish which external systems it writes to, sends to or changes, and verify with test accounts or repos before production.
- Not found in source: rollback or recovery pathBack up first, or work on a git branch or snapshot, so its changes can be undone.
- Not found in source: verifiable attributionInstall from the official repo or registry and check the publisher and URL to avoid look-alike packages.
- The repository is a resource list, not an executable Agent product; assessment is based on static files, actual functionality not verified.
- Internal link inconsistencies may affect navigation.
- No dependency management or security documentation; users should assess risks of external resources themselves.