NirDiamant/Prompt_Engineering

Prompt Engineering Techniques: Comprehensive Repository for Development and Implementation 🖋️

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.

🎓 From prompts to production

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Introduction

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.

📖 The book version of this repo

Prompt Engineering from Zero to Hero - all 22 techniques from this repo, fully explained with step-by-step breakdowns and hands-on exercises.

Amazon Kindle - $9.99 · Paperback · Gumroad PDF

Related Projects

🔍 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.

Join the community

Contributions make this better - propose ideas, share techniques, or give feedback via CONTRIBUTING.md.

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Key Features

• 🎓 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

Prompt Engineering Techniques

Explore our extensive list of prompt engineering techniques, ranging from basic to advanced:

🌱 Fundamental Concepts

1. Introduction to Prompt Engineering

   Overview 🔎

   A comprehensive introduction to the fundamental concepts of prompt engineering in the context of AI and language models.

   Implementation 🛠️

   Combines theoretical explanations with practical demonstrations, covering basic concepts, structured prompts, comparative analysis, and problem-solving applications.

2. Basic Prompt Structures

   Overview 🔎

   Explores two fundamental types of prompt structures: single-turn prompts and multi-turn prompts (conversations).

   Implementation 🛠️

   Uses OpenAI's GPT model and LangChain to demonstrate single-turn and multi-turn prompts, prompt templates, and conversation chains.

3. Prompt Templates and Variables

   Overview 🔎

   Introduces creating and using prompt templates with variables, focusing on Python and the Jinja2 templating engine.

   Implementation 🛠️

   Covers template creation, variable insertion, conditional content, list processing, and integration with the OpenAI API.

🔧 Core Techniques

4. Zero-Shot Prompting

   Overview 🔎

   Explores zero-shot prompting, allowing language models to perform tasks without specific examples or prior training.

   Implementation 🛠️

   Demonstrates direct task specification, role-based prompting, format specification, and multi-step reasoning using OpenAI and LangChain.

5. Few-Shot Learning and In-Context Learning

   Overview 🔎

   Covers Few-Shot Learning and In-Context Learning techniques using OpenAI's GPT models and the LangChain library.

   Implementation 🛠️

   Implements basic and advanced few-shot learning, in-context learning, and best practices for example selection and evaluation.

6. Chain of Thought (CoT) Prompting

   Overview 🔎

   Introduces Chain of Thought (CoT) prompting, encouraging AI models to break down complex problems into step-by-step reasoning processes.

   Implementation 🛠️

   Covers basic and advanced CoT techniques, applying them to various problem-solving scenarios and comparing results with standard prompts.

🔍 Advanced Strategies

7. Self-Consistency and Multiple Paths of Reasoning

   Overview 🔎

   Explores techniques for generating diverse reasoning paths and aggregating results to improve AI-generated answers.

   Implementation 🛠️

   Demonstrates designing diverse reasoning prompts, generating multiple responses, implementing aggregation methods, and applying self-consistency checks.

8. Constrained and Guided Generation

   Overview 🔎

   Focuses on techniques to set up constraints for model outputs and implement rule-based generation.

   Implementation 🛠️

   Uses LangChain's PromptTemplate for structured prompts, implements constraints, and explores rule-based generation techniques.

9. Role Prompting

   Overview 🔎

   Explores assigning specific roles to AI models and crafting effective role descriptions.

   Implementation 🛠️

   Demonstrates creating role-based prompts, assigning roles to AI models, and refining role descriptions for various scenarios.

🚀 Advanced Implementations

10. Task Decomposition in Prompts

    Overview 🔎

    Explores techniques for breaking down complex tasks and chaining subtasks in prompts.

    Implementation 🛠️

    Covers problem analysis, subtask definition, targeted prompt engineering, sequential execution, and result synthesis.

11. Prompt Chaining and Sequencing

    Overview 🔎

    Demonstrates how to connect multiple prompts and build logical flows for complex AI-driven tasks.

    Implementation 🛠️

    Explores basic prompt chaining, sequential prompting, dynamic prompt generation, and error handling within prompt chains.

12. Instruction Engineering

    Overview 🔎

    Focuses on crafting clear and effective instructions for language models, balancing specificity and generality.

    Implementation 🛠️

    Covers creating and refining instructions, experimenting with different structures, and implementing iterative improvement based on model responses.

🎨 Optimization and Refinement

13. Prompt Optimization Techniques

    Overview 🔎

    Explores advanced techniques for optimizing prompts, focusing on A/B testing and iterative refinement.

    Implementation 🛠️

    Demonstrates A/B testing of prompts, iterative refinement processes, and performance evaluation using relevant metrics.

14. Handling Ambiguity and Improving Clarity

    Overview 🔎

    Focuses on identifying and resolving ambiguous prompts and techniques for writing clearer prompts.

    Implementation 🛠️

    Covers analyzing ambiguous prompts, implementing strategies to resolve ambiguity, and exploring techniques for writing clearer prompts.

15. Prompt Length and Complexity Management

    Overview 🔎

    Explores techniques for managing prompt length and complexity when working with large language models.

    Implementation 🛠️

    Demonstrates techniques for balancing detail and conciseness, and strategies for handling long contexts including chunking, summarization, and iterative processing.

🛠️ Specialized Applications

16. Negative Prompting and Avoiding Undesired Outputs

    Overview 🔎

    Explores negative prompting and techniques for avoiding undesired outputs from large language models.

    Implementation 🛠️

    Covers basic negative examples, explicit exclusions, constraint implementation using LangChain, and methods for evaluating and refining negative prompts.

17. Prompt Formatting and Structure

    Overview 🔎

    Explores various prompt formats and structural elements, demonstrating their impact on AI model responses.

    Implementation 🛠️

    Demonstrates creating various prompt formats, incorporating structural elements, and comparing responses from different prompt structures.

18. Prompts for Specific Tasks

    Overview 🔎

    Explores the creation and use of prompts for specific tasks: text summarization, question-answering, code generation, and creative writing.

    Implementation 🛠️

    Covers designing task-specific prompt templates, implementing them using LangChain, executing with sample inputs, and analyzing outputs for each task type.

🌍 Advanced Applications

19. Multilingual and Cross-lingual Prompting

    Overview 🔎

    Explores techniques for designing prompts that work effectively across multiple languages and for language translation tasks.

    Implementation 🛠️

    Covers creating multilingual prompts, implementing language detection and adaptation, designing cross-lingual translation prompts, and handling various writing systems and scripts.

20. Ethical Considerations in Prompt Engineering

    Overview 🔎

    Explores the ethical dimensions of prompt engineering, focusing on avoiding biases and creating inclusive and fair prompts.

    Implementation 🛠️

    Covers identifying biases in prompts, implementing strategies to create inclusive prompts, and methods to evaluate and improve the ethical quality of AI outputs.

21. Prompt Security and Safety

    Overview 🔎

    Focuses on preventing prompt injections and implementing content filters in prompts for safe and secure AI applications.

    Implementation 🛠️

    Covers techniques for prompt injection prevention, content filtering implementation, and testing the effectiveness of security and safety measures.

22. Evaluating Prompt Effectiveness

    Overview 🔎

    Explores methods and techniques for evaluating the effectiveness of prompts in AI language models.

    Implementation 🛠️

    Covers setting up evaluation metrics, implementing manual and automated evaluation techniques, and providing practical examples using OpenAI and LangChain.

Getting Started

To begin exploring and implementing prompt engineering techniques:

1. Clone this repository:

   git clone https://github.com/NirDiamant/Prompt_Engineering.git
   

2. Navigate to the technique you're interested in:

   cd all_prompt_engineering_techniques
   

3. Follow the detailed implementation guide in each technique's notebook.

📚 Recommended reading

This list contains Amazon affiliate links. As an Amazon Associate I earn from qualifying purchases. Every book below is one I've read and genuinely recommend to engineers working in this space. The companion book to this repo is featured separately at the top of this README.

• Build a Large Language Model (From Scratch) by Sebastian Raschka. Build a GPT-style model end to end in PyTorch.
• AI Engineering: Building Applications with Foundation Models by Chip Huyen. Canonical reference for productionizing foundation-model apps.
• Hands-On Large Language Models by Jay Alammar and Maarten Grootendorst. Visual, practical LLM walkthroughs.
• Natural Language Processing with Transformers by Lewis Tunstall, Leandro von Werra, and Thomas Wolf. From the Hugging Face team.
• Designing Machine Learning Systems by Chip Huyen. ML systems in production, still the standard reference.

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Run a course, newsletter, or dev community? You can earn 25% recommending the books to your audience.

Contributing

We welcome contributions from the community! If you have a new technique or improvement to suggest:

1. Fork the repository
2. Create your feature branch: git checkout -b feature/AmazingFeature
3. Commit your changes: git commit -m 'Add some AmazingFeature'
4. Push to the branch: git push origin feature/AmazingFeature
5. Open a pull request

License

This project is licensed under a custom non-commercial license - see the LICENSE file for details.

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Keywords: Prompt Engineering, AI, Machine Learning, Natural Language Processing, LLM, Language Models, NLP, Conversational AI, Zero-Shot Learning, Few-Shot Learning, Chain of Thought