claude-skill

Unlock AI with Prompt_Engineering

Name: Unlock AI with Prompt_Engineering
Author: NirDiamant

Get 22 prompt engineering techniques with tutorials for startup founders leveraging LLMs, backed by 7.6k+ GitHub stars.

intermediate⏱ 30 minutes💵 Free

7,691 stars987 forksJupyter NotebookHealth Score 9/10Updated 7/14/2026100% free · open source

What it is

Learn 22 techniques for crafting effective instructions that supercharge AI tools.

What you can make with it

Automations like: asking Claude to create personalized product summaries from customer feedback.

How it helps

Prompt Engineering enables you to squeeze more value from LLMs like Claude, by teaching you how to design optimal prompts.

Real use case example

"A founder building a new conversational product wants to get the most out of Claude. They start by reading through the 22 prompt engineering techniques, then test out ' chaining multiple requests' to get a summary of customer feedback. After implementing this, they notice a 20% reduction in support requests."

If you're new

Beginners who want to start working with AI tools should pick this up, as it lays the groundwork.

If you're senior

Senior engineers and pros will reach for this as a reference when fine-tuning their Claude workflows.

Common confusion cleared up

This skill isn't about 'telling' Claude what to do, but rather crafting prompts that coax the most accurate and helpful output from it.

Best inside these AI tools

Claude DesktopClaude CodeClaude API

Pairs with

Claude APIJupyter Notebook

Why we list it on WorkflowStacks: This skill is here because it's a must-have foundation for working with AI tools, and it's free and open-source.

What it does

Provides 22 prompt engineering techniques with hands-on Jupyter Notebook tutorials to help startup founders effectively leverage Large Language Models (LLMs)

Install / run

Clone the repository using `git clone https://github.com/NirDiamant/Prompt_Engineering.git`

When to use it

•When you need to optimize prompts for better LLM responses
•When experimenting with different prompt engineering techniques to improve model performance
•When seeking hands-on tutorials and examples for advanced LLM strategies

Quick start

1Open the cloned repository in a Jupyter Notebook environment
2Navigate to the `tutorials` directory and open a specific technique's notebook (e.g., `01_Fundamental_Concepts.ipynb`)
3Run the notebook cells to explore the technique and its implementation
4Modify the prompts and parameters in the notebook to experiment with different techniques
5Reference the `README.md` for an overview of the 22 techniques and tutorials available

Ready-to-paste prompt

Open `05_Primer_Prompt_Engineering.ipynb` and run the cell containing `prompt = 'Explain the concept of prompt engineering and its importance in LLMs.'` to see a primer on prompt engineering

Heads up: Ensure you have Jupyter Notebook installed and configured properly, as the tutorials are designed to be run in this environment, and some techniques may require specific LLM models or APIs to be effective

Saves to your device

Topics

chain-of-thought

chatgpt

claude

few-shot-learning

genai

generative-ai

gpt

in-context-learning

langchain

llm

llms

machine-learning

openai

prompt-engineering

prompting

python

tutorials

How Unlock AI with Prompt_Engineering works

Codeflow

Free to inspect

Unlock AI with Prompt_Engineering is a documents-only repository (3 doc files) — something you read, not something you run. There is nothing to install. Last commit this month, Other license.

Size

Documents only

3 documents · 8 min skim

Setup

No code to run

It's prompts / instructions. Copy them into your AI tool.

Runs on

Inside your AI tool

Helper scripts use Python

Jupyter Notebook 100%

Where to start reading