GH Repository · NirDiamant
Prompt_Engineering
22 prompt engineering techniques with hands-on Jupyter Notebook tutorials, from fundamental concepts to advanced strategies for leveraging LLMs.
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Jupyter Notebookmachine-learninggptfew-shot-learningaiclaudeagent-frameworkprompt-engineeringtutorialsllmpythongenaillmschatgptlangchainopenaiin-context-learningpromptinggenerative-aichain-of-thought
A collection of 22 prompt engineering techniques presented as hands-on Jupyter Notebook tutorials, covering fundamentals through advanced strategies for working with LLMs. Each technique is demonstrated in a runnable notebook rather than described only in prose.
You want to learn or reference concrete prompting techniques with executable examples you can run and modify yourself.
Use it to
- Learn prompt engineering fundamentals through runnable notebooks
- Study advanced techniques like chain-of-thought and few-shot learning
- Adapt tutorial notebooks as templates for your own LLM prompts
- Compare prompting approaches across models like ChatGPT and Claude
- Use as a curriculum for teaching prompt engineering
For Developers and practitioners learning to prompt LLMs effectively
- Role
- agent-framework
- Language
- Jupyter Notebook
- Licence
- custom (licence file present)
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- 1,023
- Last push
- 2026-09-15
topicsprompt-engineeringllmstutorialsjupyter-notebookspythongenerative-ai