Prompt_Engineering
Hands-on Jupyter Notebook tutorials for prompt engineering with LLMs
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Decision brief
The Prompt_Engineering repository provides hands-on Jupyter Notebook tutorials that guide users through 22 prompt engineering techniques for advanced use of Language Learning Models.
Good fit when
- When you need practical, step-by-step guidance in Jupyter Notebooks to understand and implement prompt engineering techniques with LLMs.
- For developers looking to improve their proficiency specifically with the Chain-of-Thought technique or other nuanced strategies outlined in the detailed tutorials.
Avoid when
- If you prefer interactive tooling over manual notebook work, as the repository is heavily based on self-guided Jupyter Notebook exercises.
- This repository may not be suitable if you are focused exclusively on specific LLM frameworks like Hugging Face Transformers or SpaCy that it does not emphasize.
Observed Jul 17, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Active (13d since push)
- As of 4w
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- Not a fork · Personal account
- As of 4w
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Install
git clone https://github.com/NirDiamant/Prompt_EngineeringSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
This repository offers practical instructions and examples through Jupyter Notebooks to master various prompt engineering techniques designed for advanced utilization of Language Learning Models.
Capability facts
- Languages
- jupyter notebook
Source: github.language · Jul 28, 2026
Categories
Tags
README
Getting Started
To begin exploring and implementing prompt engineering techniques:
- Clone this repository:
git clone https://github.com/NirDiamant/Prompt_Engineering.git - Navigate to the technique you're interested in:
cd all_prompt_engineering_techniques - Follow the detailed implementation guide in each technique's notebook.
License
This project is licensed under a custom non-commercial license - see the LICENSE file for details.
⭐️ If you find this repository helpful, please consider giving it a star!
Keywords: Prompt Engineering, AI, Machine Learning, Natural Language Processing, LLM, Language Models, NLP, Conversational AI, Zero-Shot Learning, Few-Shot Learning, Chain of Thought
For agents
This page has a .md twin and JSON over the API.