# Prompt Engineering Techniques — 20+ Methods

> Comprehensive prompt engineering collection with 20+ techniques. 7.3K+ stars. Chain-of-Thought, ReAct, Tree-of-Thought, runnable notebooks.

## Install

Paste the prompt below into your AI tool:

# Prompt Engineering Techniques — 20+ Methods

> Comprehensive collection of prompt engineering tutorials with runnable implementations. 7.3K+ GitHub stars. From basics to advanced strategies.

## Quick Use

Pick a technique, copy the template, adapt to your task:

**Chain-of-Thought (CoT):**
```
Solve this step by step:
1. First, identify the key variables
2. Then, set up the equation
3. Finally, solve and verify

Problem: [your problem here]
```

**ReAct (Reasoning + Acting):**
```
Answer the following question by alternating between Thought, Action, and Observation steps.

Question: [your question]

Thought 1: I need to...
Action 1: Search for...
Observation 1: [result]
Thought 2: Based on this, I should...
```

**Self-Consistency:**
```
Generate 5 different solutions to this problem, then select the most common answer:

Problem: [your problem]

Solution 1: ...
Solution 2: ...
[Let the model generate multiple paths, then pick the majority]
```

## Intro

This is a curated collection of 20+ prompt engineering techniques, each with explanations, templates, and runnable Jupyter notebooks. Covers:

**Foundational:**
- Zero-Shot Prompting — No examples needed, just clear instructions
- Few-Shot Prompting — Provide examples for the model to follow
- Chain-of-Thought (CoT) — Step-by-step reasoning
- Role Prompting — Assign an expert persona

**Intermediate:**
- ReAct — Combine reasoning with tool use
- Self-Consistency — Multiple reasoning paths, majority vote
- Tree-of-Thought — Explore multiple solution branches
- Least-to-Most — Break complex problems into sub-problems

**Advanced:**
- Constitutional AI Prompting — Self-critique and revision
- Meta-Prompting — Prompts that generate prompts
- Automatic Prompt Engineering (APE) — Let the model optimize its own prompts
- Prompt Chaining — Sequential prompts where output feeds input
- Skeleton-of-Thought — Generate outline first, then fill in details

Each technique includes: concept explanation, when to use it, template, Python notebook, and real-world examples.

## FAQ

**Q: Which technique should I start with?**
A: Chain-of-Thought for reasoning tasks, Few-Shot for format-sensitive tasks, ReAct for tasks requiring external tool use.

**Q: Do these work with all LLMs?**
A: Yes. These are model-agnostic techniques. They work with GPT-4, Claude, Gemini, Llama, Mistral, and any instruction-following LLM.

**Q: Can I use these in production?**
A: Absolutely. Each technique is production-tested. The notebooks include evaluation metrics so you can measure effectiveness.

## Works With

- Any LLM: Claude, GPT-4, Gemini, Llama, Mistral
- Claude Code, Cursor, Codex (for coding-specific prompts)
- LangChain, LlamaIndex (for chained prompts)
- Jupyter Notebook (runnable examples)

## Source & Thanks

- GitHub: https://github.com/NirDiamant/Prompt_Engineering (7.3K+ stars)
- Author: Nir Diamant
- License: Open Source


---
Source: https://tokrepo.com/en/workflows/de151579-8446-4ffc-b6e2-ec0719eb1561
Author: Prompt Lab