Prompt_Engineering
This repository offers a comprehensive collection of tutorials and implementations for Prompt Engineering techniques, ranging from fundamental concepts to advanced strategies. It serves as an essential resource for mastering the art of effectively communicating with and leveraging large language models in AI applications.
Stars: 2546
Prompt Engineering Techniques is a comprehensive repository for learning, building, and sharing prompt engineering techniques, from basic concepts to advanced strategies for leveraging large language models. It provides step-by-step tutorials, practical implementations, and a platform for showcasing innovative prompt engineering techniques. The repository covers fundamental concepts, core techniques, advanced strategies, optimization and refinement, specialized applications, and advanced applications in prompt engineering.
README:
π Support This Project: Your sponsorship fuels innovation in prompt engineering development. Become a sponsor to help maintain and expand this valuable resource!
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.
π Cutting-edge Updates |
π‘ Expert Insights |
π― Top 0.1% Content |
*Join thousands of AI enthusiasts getting unique cutting-edge insights and free tutorials! Plus, subscribers get exclusive early access and special discounts to our upcoming RAG Techniques course! *
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.
π Explore my comprehensive guide on RAG techniques to learn how to enhance AI systems with external knowledge retrieval, complementing language model capabilities with rich, up-to-date information.
π€ Dive into my GenAI Agents Repository for a wide range of AI agent implementations and tutorials, from simple conversational bots to complex, multi-agent systems for various applications.
This repository grows stronger with your contributions! Join our vibrant Discord community β the central hub for shaping and advancing this project together π€
Whether you're a novice eager to learn or an expert ready to share your knowledge, your insights can shape the future of prompt engineering. Join us to propose ideas, get feedback, and collaborate on innovative implementations. For contribution guidelines, please refer to our CONTRIBUTING.md file. Let's advance prompt engineering technology together!
π For discussions on GenAI, or to explore knowledge-sharing opportunities, feel free to connect on LinkedIn.
- π Learn prompt engineering techniques from beginner to advanced levels
- π§ Explore a wide range of prompt structures and applications
- π Step-by-step tutorials and comprehensive documentation
- π οΈ Practical, ready-to-use prompt implementations
- π Regular updates with the latest advancements in prompt engineering
- π€ Share your own prompt engineering creations with the community
Explore our extensive list of prompt engineering techniques, ranging from basic to advanced:
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
βοΈ If you find this repository helpful, please consider giving it a star!
Keywords: Prompt Engineering, AI, Machine Learning, Natural Language Processing, LLM, Language Models, NLP, Conversational AI, Zero-Shot Learning, Few-Shot Learning, Chain of Thought
For Tasks:
Click tags to check more tools for each tasksFor Jobs:
Alternative AI tools for Prompt_Engineering
Similar Open Source Tools
Prompt_Engineering
Prompt Engineering Techniques is a comprehensive repository for learning, building, and sharing prompt engineering techniques, from basic concepts to advanced strategies for leveraging large language models. It provides step-by-step tutorials, practical implementations, and a platform for showcasing innovative prompt engineering techniques. The repository covers fundamental concepts, core techniques, advanced strategies, optimization and refinement, specialized applications, and advanced applications in prompt engineering.
GenAI_Agents
GenAI Agents is a comprehensive repository for developing and implementing Generative AI (GenAI) agents, ranging from simple conversational bots to complex multi-agent systems. It serves as a valuable resource for learning, building, and sharing GenAI agents, offering tutorials, implementations, and a platform for showcasing innovative agent creations. The repository covers a wide range of agent architectures and applications, providing step-by-step tutorials, ready-to-use implementations, and regular updates on advancements in GenAI technology.
ConvoForm
ConvoForm.com transforms traditional forms into interactive conversational experiences, powered by AI for an enhanced user journey. It offers AI-Powered Form Generation, Real-time Form Editing and Preview, and Customizable Submission Pages. The tech stack includes Next.js for frontend, tRPC for backend, GPT-3.5-Turbo for AI integration, and Socket.io for real-time updates. Local setup requires Node.js, pnpm, Git, PostgreSQL database, Clerk for Authentication, OpenAI key, Redis Database, and Sentry for monitoring. The project is open for contributions and is licensed under the MIT License.
llmops-duke-aipi
LLMOps Duke AIPI is a course focused on operationalizing Large Language Models, teaching methodologies for developing applications using software development best practices with large language models. The course covers various topics such as generative AI concepts, setting up development environments, interacting with large language models, using local large language models, applied solutions with LLMs, extensibility using plugins and functions, retrieval augmented generation, introduction to Python web frameworks for APIs, DevOps principles, deploying machine learning APIs, LLM platforms, and final presentations. Students will learn to build, share, and present portfolios using Github, YouTube, and Linkedin, as well as develop non-linear life-long learning skills. Prerequisites include basic Linux and programming skills, with coursework available in Python or Rust. Additional resources and references are provided for further learning and exploration.
ThereForYou
ThereForYou is a groundbreaking solution aimed at enhancing public safety, particularly focusing on mental health support and suicide prevention. Leveraging cutting-edge technologies such as artificial intelligence (AI), machine learning (ML), natural language processing (NLP), and blockchain, the project offers accessible and empathetic assistance to individuals facing mental health challenges.
awesome-gpt-security
Awesome GPT + Security is a curated list of awesome security tools, experimental case or other interesting things with LLM or GPT. It includes tools for integrated security, auditing, reconnaissance, offensive security, detecting security issues, preventing security breaches, social engineering, reverse engineering, investigating security incidents, fixing security vulnerabilities, assessing security posture, and more. The list also includes experimental cases, academic research, blogs, and fun projects related to GPT security. Additionally, it provides resources on GPT security standards, bypassing security policies, bug bounty programs, cracking GPT APIs, and plugin security.
AI-Gateway
The AI-Gateway repository explores the AI Gateway pattern through a series of experimental labs, focusing on Azure API Management for handling AI services APIs. The labs provide step-by-step instructions using Jupyter notebooks with Python scripts, Bicep files, and APIM policies. The goal is to accelerate experimentation of advanced use cases and pave the way for further innovation in the rapidly evolving field of AI. The repository also includes a Mock Server to mimic the behavior of the OpenAI API for testing and development purposes.
groqbook
Groqbook is a streamlit app that quickly generates entire books from a one-line prompt using Llama3 on Groq. It focuses on nonfiction books, generating chapters within seconds by utilizing Llama3-8b and Llama3-70b models. The tool currently uses section titles to create chapter content, with plans to expand to full book context for fiction books. Users can download the book contents in a text file, and the app supports markdown styling with tables and code for an aesthetic book display.
long-llms-learning
A repository sharing the panorama of the methodology literature on Transformer architecture upgrades in Large Language Models for handling extensive context windows, with real-time updating the newest published works. It includes a survey on advancing Transformer architecture in long-context large language models, flash-ReRoPE implementation, latest news on data engineering, lightning attention, Kimi AI assistant, chatglm-6b-128k, gpt-4-turbo-preview, benchmarks like InfiniteBench and LongBench, long-LLMs-evals for evaluating methods for enhancing long-context capabilities, and LLMs-learning for learning technologies and applicated tasks about Large Language Models.
arcadia
Arcadia is an all-in-one enterprise-grade LLMOps platform that provides a unified interface for developers and operators to build, debug, deploy, and manage AI agents. It supports various LLMs, embedding models, reranking models, and more. Built on langchaingo (golang) for better performance and maintainability. The platform follows the operator pattern that extends Kubernetes APIs, ensuring secure and efficient operations.
crewAI-examples
crewAI-examples is a repository containing examples demonstrating the usage of crewAI framework for facilitating collaboration of role-playing AI agents. The examples showcase various ways to automate processes using crewAI. Created by @joaomdmoura.
langwatch
LangWatch is a monitoring and analytics platform designed to track, visualize, and analyze interactions with Large Language Models (LLMs). It offers real-time telemetry to optimize LLM cost and latency, a user-friendly interface for deep insights into LLM behavior, user analytics for engagement metrics, detailed debugging capabilities, and guardrails to monitor LLM outputs for issues like PII leaks and toxic language. The platform supports OpenAI and LangChain integrations, simplifying the process of tracing LLM calls and generating API keys for usage. LangWatch also provides documentation for easy integration and self-hosting options for interested users.
crewAI
crewAI is a cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks. It provides a flexible and structured approach to AI collaboration, enabling users to define agents with specific roles, goals, and tools, and assign them tasks within a customizable process. crewAI supports integration with various LLMs, including OpenAI, and offers features such as autonomous task delegation, flexible task management, and output parsing. It is open-source and welcomes contributions, with a focus on improving the library based on usage data collected through anonymous telemetry.
BotSharp
BotSharp is an open-source machine learning framework for building AI bot platforms. It provides a comprehensive set of tools and components for developing and deploying intelligent virtual assistants. BotSharp is designed to be modular and extensible, allowing developers to easily integrate it with their existing systems and applications. With BotSharp, you can quickly and easily create AI-powered chatbots, virtual assistants, and other conversational AI applications.
FrugalGPT
FrugalGPT is a framework that offers techniques for building Large Language Model (LLM) applications with budget constraints. It provides a cost-effective solution for utilizing LLMs while maintaining performance. The framework includes support for various models and offers resources for reducing costs and improving efficiency in LLM applications.
examples
Cerebrium's official examples repository provides practical, ready-to-use examples for building Machine Learning / AI applications on the platform. The repository contains self-contained projects demonstrating specific use cases with detailed instructions on deployment. Examples cover a wide range of categories such as getting started, advanced concepts, endpoints, integrations, large language models, voice, image & video, migrations, application demos, batching, and Python apps.
For similar tasks
ai-commits-intellij-plugin
AI Commits is a plugin for IntelliJ-based IDEs and Android Studio that generates commit messages using git diff and OpenAI. It offers features such as generating commit messages from diff using OpenAI API, computing diff only from selected files and lines in the commit dialog, creating custom prompts for commit message generation, using predefined variables and hints to customize prompts, choosing any of the models available in OpenAI API, setting OpenAI network proxy, and setting custom OpenAI compatible API endpoint.
extensionOS
Extension | OS is an open-source browser extension that brings AI directly to users' web browsers, allowing them to access powerful models like LLMs seamlessly. Users can create prompts, fix grammar, and access intelligent assistance without switching tabs. The extension aims to revolutionize online information interaction by integrating AI into everyday browsing experiences. It offers features like Prompt Factory for tailored prompts, seamless LLM model access, secure API key storage, and a Mixture of Agents feature. The extension was developed to empower users to unleash their creativity with custom prompts and enhance their browsing experience with intelligent assistance.
img-prompt
IMGPrompt is an AI prompt editor tailored for image and video generation tools like Stable Diffusion, Midjourney, DALLΒ·E, FLUX, and Sora. It offers a clean interface for viewing and combining prompts with translations in multiple languages. The tool includes features like smart recommendations, translation, random color generation, prompt tagging, interactive editing, categorized tag display, character count, and localization. Users can enhance their creative workflow by simplifying prompt creation and boosting efficiency.
5ire
5ire is a cross-platform desktop client that integrates a local knowledge base for multilingual vectorization, supports parsing and vectorization of various document formats, offers usage analytics to track API spending, provides a prompts library for creating and organizing prompts with variable support, allows bookmarking of conversations, and enables quick keyword searches across conversations. It is licensed under the GNU General Public License version 3.
sidecar
Sidecar is the AI brains of Aide the editor, responsible for creating prompts, interacting with LLM, and ensuring seamless integration of all functionalities. It includes 'tool_box.rs' for handling language-specific smartness, 'symbol/' for smart and independent symbols, 'llm_prompts/' for creating prompts, and 'repomap' for creating a repository map using page rank on code symbols. Users can contribute by submitting bugs, feature requests, reviewing source code changes, and participating in the development workflow.
labs-ai-tools-for-devs
This repository provides AI tools for developers through Docker containers, enabling agentic workflows. It allows users to create complex workflows using Dockerized tools and Markdown, leveraging various LLM models. The core features include Dockerized tools, conversation loops, multi-model agents, project-first design, and trackable prompts stored in a git repo.
Prompt_Engineering
Prompt Engineering Techniques is a comprehensive repository for learning, building, and sharing prompt engineering techniques, from basic concepts to advanced strategies for leveraging large language models. It provides step-by-step tutorials, practical implementations, and a platform for showcasing innovative prompt engineering techniques. The repository covers fundamental concepts, core techniques, advanced strategies, optimization and refinement, specialized applications, and advanced applications in prompt engineering.
log10
Log10 is a one-line Python integration to manage your LLM data. It helps you log both closed and open-source LLM calls, compare and identify the best models and prompts, store feedback for fine-tuning, collect performance metrics such as latency and usage, and perform analytics and monitor compliance for LLM powered applications. Log10 offers various integration methods, including a python LLM library wrapper, the Log10 LLM abstraction, and callbacks, to facilitate its use in both existing production environments and new projects. Pick the one that works best for you. Log10 also provides a copilot that can help you with suggestions on how to optimize your prompt, and a feedback feature that allows you to add feedback to your completions. Additionally, Log10 provides prompt provenance, session tracking and call stack functionality to help debug prompt chains. With Log10, you can use your data and feedback from users to fine-tune custom models with RLHF, and build and deploy more reliable, accurate and efficient self-hosted models. Log10 also supports collaboration, allowing you to create flexible groups to share and collaborate over all of the above features.
For similar jobs
sweep
Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.
teams-ai
The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.
ai-guide
This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.
classifai
Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.
chatbot-ui
Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.
BricksLLM
BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students
uAgents
uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.
griptape
Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.