NirDiamant/Prompt_Engineering resource

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7818 stars · 1018 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A collection of 22 hands-on Jupyter Notebook tutorials covering prompt engineering techniques for large language models, from basic prompt templates to advanced strategies like chain-of-thought, self-consistency, and tree-of-thought prompting. It serves as an educational resource for learning and implementing prompting methods with code examples.

Use cases

• learn prompt engineering from scratch
• understand chain-of-thought prompting with code examples
• compare few-shot vs zero-shot prompting techniques
• implement tree-of-thought and self-consistency prompting
• find practical LLM prompting patterns for building applications
• study in-context learning strategies
• get hands-on notebooks for ChatGPT and Claude prompting

When to choose

• you want hands-on, runnable notebooks to learn prompting techniques
• you need a structured progression from basic to advanced prompt engineering
• you prefer code-first tutorials over theory-only articles
• you want coverage of many techniques (22) in one repository

When to avoid

• you need a production prompt management or evaluation tool rather than tutorials
• you want a library or SDK to integrate into your codebase
• you need non-Python examples or a non-notebook learning format
• you require guaranteed licensing terms for commercial reuse (license is non-standard)

Facets

learning-resource · maturity active

prompt-engineering llm-inference nlp large-language-models tutorials artificial-intelligence python cross-platform jupyter-notebooks chain-of-thought few-shot-learning genai hands-on-tutorials openai langchain natural-language-processing

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