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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.
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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.
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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:
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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.
Covers creating and refining instructions, experimenting with different structures, and implementing iterative improvement based on model responses.
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Prompt Optimization Techniques
Explores advanced techniques for optimizing prompts, focusing on A/B testing and iterative refinement.
Demonstrates A/B testing of prompts, iterative refinement processes, and performance evaluation using relevant metrics.
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Handling Ambiguity and Improving Clarity
Focuses on identifying and resolving ambiguous prompts and techniques for writing clearer prompts.
Covers analyzing ambiguous prompts, implementing strategies to resolve ambiguity, and exploring techniques for writing clearer prompts.
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Prompt Length and Complexity Management
Explores techniques for managing prompt length and complexity when working with large language models.
Demonstrates techniques for balancing detail and conciseness, and strategies for handling long contexts including chunking, summarization, and iterative processing.
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Negative Prompting and Avoiding Undesired Outputs
Explores negative prompting and techniques for avoiding undesired outputs from large language models.
Covers basic negative examples, explicit exclusions, constraint implementation using LangChain, and methods for evaluating and refining negative prompts.
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Prompt Formatting and Structure
Explores various prompt formats and structural elements, demonstrating their impact on AI model responses.
Demonstrates creating various prompt formats, incorporating structural elements, and comparing responses from different prompt structures.
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Explores the creation and use of prompts for specific tasks: text summarization, question-answering, code generation, and creative writing.
Covers designing task-specific prompt templates, implementing them using LangChain, executing with sample inputs, and analyzing outputs for each task type.
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Multilingual and Cross-lingual Prompting
Explores techniques for designing prompts that work effectively across multiple languages and for language translation tasks.
Covers creating multilingual prompts, implementing language detection and adaptation, designing cross-lingual translation prompts, and handling various writing systems and scripts.
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Ethical Considerations in Prompt Engineering
Explores the ethical dimensions of prompt engineering, focusing on avoiding biases and creating inclusive and fair prompts.
Covers identifying biases in prompts, implementing strategies to create inclusive prompts, and methods to evaluate and improve the ethical quality of AI outputs.
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Focuses on preventing prompt injections and implementing content filters in prompts for safe and secure AI applications.
Covers techniques for prompt injection prevention, content filtering implementation, and testing the effectiveness of security and safety measures.
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Evaluating Prompt Effectiveness
Explores methods and techniques for evaluating the effectiveness of prompts in AI language models.
Covers setting up evaluation metrics, implementing manual and automated evaluation techniques, and providing practical examples using OpenAI and LangChain.
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.
We welcome contributions from the community! If you have a new technique or improvement to suggest:
- Fork the repository
- Create your feature branch:
git checkout -b feature/AmazingFeature - Commit your changes:
git commit -m 'Add some AmazingFeature' - Push to the branch:
git push origin feature/AmazingFeature - Open a pull request
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