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Welcome to one of the most extensive and dynamic collections of Prompt Engineering tutorials and implementations available today. This repository serves as a comprehensive resource for learning, building, and sharing prompt engineering techniques, ranging from basic concepts to advanced strategies for leveraging large language models.
22 hands-on tutorials covering everything from basic prompt templates to advanced techniques like chain-of-thought, self-consistency, and tree-of-thought prompting.
Prompt Engineering: Master the Art of AI Interaction - all 22 techniques from this repo, fully explained with step-by-step breakdowns and hands-on exercises.
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How LLMs Actually Work (and Why AI Makes Things Up) recalling a fact and inventing one are literally the same move |
Context Is the New Code the shift from writing the code to shaping what the model sees |
Stop Thinking Claude Code Is Magic. Here's How It Works what the agent loop is actually doing on every turn |
Prompt engineering is at the forefront of artificial intelligence, revolutionizing the way we interact with and leverage AI technologies. This repository is designed to guide you through the development journey, from basic prompt structures to advanced, cutting-edge techniques.
Our goal is to provide a valuable resource for everyone - from beginners taking their first steps in AI to seasoned practitioners pushing the boundaries of what's possible. By offering a range of examples from foundational to complex, we aim to facilitate learning, experimentation, and innovation in the rapidly evolving field of prompt engineering.
Furthermore, this repository serves as a platform for showcasing innovative prompt engineering techniques. Whether you've developed a novel approach or found an innovative application for existing techniques, we encourage you to share your work with the community.
🔍 RAG Techniques - 40+ notebooks on retrieval-augmented generation.
🤖 GenAI Agents - implementations and tutorials for building AI agent systems.
🚀 Agents Towards Production - code-first tutorials for shipping production-grade agents.
🧠 Agent Memory Techniques - 30 notebooks on agent memory: vector stores, graphs, Mem0, Zep.
Contributions make this better - propose ideas, share techniques, or give feedback via CONTRIBUTING.md.
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- 🎓 Learn prompt engineering techniques from beginner to advanced levels
- 🧠 Explore a wide range of prompt structures and applications
- 📚 Step-by-step tutorials and comprehensive documentation
- 🛠️ Practical, ready-to-use prompt implementations
- 🌟 Regular updates with the latest advancements in prompt engineering
- 🤝 Share your own prompt engineering creations with the community
Explore our extensive list of prompt engineering techniques, ranging from basic to advanced:
| # | Category | Technique | Description |
|---|---|---|---|
| 1 | 🎓 Fundamental Concepts | Introduction to Prompt Engineering | Comprehensive introduction to fundamental concepts of prompt engineering |
| 2 | 🎓 Fundamental Concepts | Basic Prompt Structures | Exploration of single-turn and multi-turn prompt structures |
| 3 | 🎓 Fundamental Concepts | Prompt Templates and Variables | Creating and using prompt templates with variables |
| 4 | 🔧 Core Techniques | Zero-Shot Prompting | Performing tasks without specific examples |
| 5 | 🔧 Core Techniques | Few-Shot Learning | Learning from a small number of examples |
| 6 | 🔧 Core Techniques | Chain of Thought (CoT) | Step-by-step reasoning processes |
| 7 | 🎯 Advanced Strategies | Self-Consistency | Multiple reasoning paths and result aggregation |
| 8 | 🎯 Advanced Strategies | Constrained Generation | Setting up output constraints |
| 9 | 🎯 Advanced Strategies | Role Prompting | Assigning specific roles to AI models |
| 10 | 🚀 Advanced Implementations | Task Decomposition | Breaking down complex tasks |
| 11 | 🚀 Advanced Implementations | Prompt Chaining | Connecting multiple prompts |
| 12 | 🚀 Advanced Implementations | Instruction Engineering | Crafting clear instructions |
| 13 | ⚡ Optimization | Prompt Optimization | A/B testing and refinement |
| 14 | ⚡ Optimization | Handling Ambiguity | Resolving ambiguous prompts |
| 15 | ⚡ Optimization | Length Management | Managing prompt complexity |
| 16 | 🛠️ Specialized Applications | Negative Prompting | Avoiding undesired outputs |
| 17 | 🛠️ Specialized Applications | Prompt Formatting | Various prompt formats |
| 18 | 🛠️ Specialized Applications | Task-Specific Prompts | Prompts for specific tasks |
| 19 | 🌍 Advanced Applications | Multilingual Prompting | Cross-lingual techniques |
| 20 | 🌍 Advanced Applications | Ethical Considerations | Bias avoidance and inclusivity |
| 21 | 🌍 Advanced Applications | Prompt Security | Preventing injections |
| 22 | 🌍 Advanced Applications | Effectiveness Evaluation | Evaluating prompt performance |
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Introduction to Prompt Engineering
A comprehensive introduction to the fundamental concepts of prompt engineering in the context of AI and language models.
Combines theoretical explanations with practical demonstrations, covering basic concepts, structured prompts, comparative analysis, and problem-solving applications.
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Explores two fundamental types of prompt structures: single-turn prompts and multi-turn prompts (conversations).
Uses OpenAI's GPT model and LangChain to demonstrate single-turn and multi-turn prompts, prompt templates, and conversation chains.
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Prompt Templates and Variables
Introduces creating and using prompt templates with variables, focusing on Python and the Jinja2 templating engine.
Covers template creation, variable insertion, conditional content, list processing, and integration with the OpenAI API.
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Explores zero-shot prompting, allowing language models to perform tasks without specific examples or prior training.
Demonstrates direct task specification, role-based prompting, format specification, and multi-step reasoning using OpenAI and LangChain.
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Few-Shot Learning and In-Context Learning
Covers Few-Shot Learning and In-Context Learning techniques using OpenAI's GPT models and the LangChain library.
Implements basic and advanced few-shot learning, in-context learning, and best practices for example selection and evaluation.
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Chain of Thought (CoT) Prompting
Introduces Chain of Thought (CoT) prompting, encouraging AI models to break down complex problems into step-by-step reasoning processes.
Covers basic and advanced CoT techniques, applying them to various problem-solving scenarios and comparing results with standard prompts.
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Self-Consistency and Multiple Paths of Reasoning
Explores techniques for generating diverse reasoning paths and aggregating results to improve AI-generated answers.
Demonstrates designing diverse reasoning prompts, generating multiple responses, implementing aggregation methods, and applying self-consistency checks.
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Constrained and Guided Generation
Focuses on techniques to set up constraints for model outputs and implement rule-based generation.
Uses LangChain's PromptTemplate for structured prompts, implements constraints, and explores rule-based generation techniques.
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Explores assigning specific roles to AI models and crafting effective role descriptions.
Demonstrates creating role-based prompts, assigning roles to AI models, and refining role descriptions for various scenarios.
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Explores techniques for breaking down complex tasks and chaining subtasks in prompts.
Covers problem analysis, subtask definition, targeted prompt engineering, sequential execution, and result synthesis.
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Prompt Chaining and Sequencing
Demonstrates how to connect multiple prompts and build logical flows for complex AI-driven tasks.
Explores basic prompt chaining, sequential prompting, dynamic prompt generation, and error handling within prompt chains.
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Focuses on crafting clear and effective instructions for language models, balancing specificity and generality.