awesome-gpt-prompt-engineering
A curated list of awesome resources, tools, and other shiny things for LLM prompt engineering.
Top Related Projects
This repo includes ChatGPT prompt curation to use ChatGPT better.
🐙 Guides, papers, lecture, notebooks and resources for prompt engineering
This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc
A library for helping developers craft prompts for Large Language Models
Examples and guides for using the OpenAI API
Quick Overview
The "awesome-gpt-prompt-engineering" repository is a curated list of resources, tools, and techniques for prompt engineering with GPT models. It aims to help developers and researchers improve their skills in crafting effective prompts for large language models, particularly focusing on OpenAI's GPT series.
Pros
- Comprehensive collection of prompt engineering resources
- Regularly updated with new content and tools
- Well-organized structure for easy navigation
- Includes both theoretical concepts and practical applications
Cons
- May be overwhelming for beginners due to the large amount of information
- Some listed resources might become outdated quickly as the field evolves
- Lacks in-depth explanations or tutorials for each resource
- Primarily focused on GPT models, potentially limiting its applicability to other language models
Note: As this is not a code library, the code example and quick start sections have been omitted as per the instructions.
Competitor Comparisons
This repo includes ChatGPT prompt curation to use ChatGPT better.
Pros of awesome-chatgpt-prompts
- Larger collection of prompts (300+) covering a wide range of topics and use cases
- Well-organized with clear categories and descriptions for each prompt
- Active community with frequent updates and contributions
Cons of awesome-chatgpt-prompts
- Focuses primarily on ChatGPT-specific prompts, potentially limiting applicability to other AI models
- Less emphasis on prompt engineering techniques and best practices
Code Comparison
awesome-chatgpt-prompts:
# Act as a Linux Terminal
I want you to act as a linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. do not write explanations. do not type commands unless I instruct you to do so. when i need to tell you something in english, i will do so by putting text inside curly brackets {like this}. my first command is pwd
awesome-gpt-prompt-engineering:
# Prompt Structure
A good prompt structure typically includes:
1. Context: Provide background information
2. Instructions: Clearly state what you want the AI to do
3. Examples: Give sample inputs and outputs if needed
4. Constraints: Specify any limitations or requirements
5. Output format: Describe how you want the response formatted
The code comparison highlights the different approaches of the two repositories. awesome-chatgpt-prompts focuses on specific, ready-to-use prompts, while awesome-gpt-prompt-engineering emphasizes prompt engineering techniques and best practices.
🐙 Guides, papers, lecture, notebooks and resources for prompt engineering
Pros of Prompt-Engineering-Guide
- More comprehensive and structured content, covering a wide range of prompt engineering topics
- Regularly updated with new techniques and best practices
- Includes practical examples and case studies for better understanding
Cons of Prompt-Engineering-Guide
- May be overwhelming for beginners due to its extensive content
- Lacks a curated list of external resources and tools
Code Comparison
While both repositories primarily focus on documentation and guidelines, Prompt-Engineering-Guide occasionally includes code snippets to illustrate concepts. For example:
Prompt-Engineering-Guide:
prompt = f"""
Translate the following English text to French: '{text}'
"""
response = openai.Completion.create(
engine="text-davinci-002",
prompt=prompt,
max_tokens=100
)
Awesome-gpt-prompt-engineering doesn't typically include code snippets, focusing instead on textual guidelines and examples.
Summary
Prompt-Engineering-Guide offers a more comprehensive and structured approach to prompt engineering, with regular updates and practical examples. However, it may be overwhelming for beginners. Awesome-gpt-prompt-engineering provides a more concise and curated list of resources, which can be beneficial for those looking for quick references and external tools.
This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc
Pros of Awesome-Prompt-Engineering
- More comprehensive coverage of prompt engineering topics, including techniques, tools, and resources
- Better organized structure with clear categories and subcategories
- Regularly updated with new content and contributions from the community
Cons of Awesome-Prompt-Engineering
- Less focus on specific GPT models and their unique characteristics
- May be overwhelming for beginners due to the large amount of information
Code Comparison
While both repositories primarily consist of curated lists and don't contain significant code samples, here's a comparison of their README structures:
Awesome-Prompt-Engineering:
# Awesome Prompt Engineering
## Table of Contents
- [Techniques](#techniques)
- [Tools](#tools)
- [Resources](#resources)
awesome-gpt-prompt-engineering:
# Awesome GPT Prompt Engineering
- [Prompts](#prompts)
- [Resources](#resources)
- [Contributing](#contributing)
The Awesome-Prompt-Engineering repository has a more detailed and structured table of contents, reflecting its broader scope and organization.
A library for helping developers craft prompts for Large Language Models
Pros of prompt-engine
- Developed and maintained by Microsoft, ensuring high-quality and industry-standard practices
- Focuses on providing a structured framework for prompt engineering, potentially offering more consistency
- Likely to have better integration with other Microsoft tools and services
Cons of prompt-engine
- May have a steeper learning curve due to its more structured approach
- Potentially less community-driven content compared to awesome-gpt-prompt-engineering
- Could be more limited in scope, focusing primarily on Microsoft's ecosystem
Code Comparison
prompt-engine:
import { PromptTemplate } from "@microsoft/prompt-engine";
const template = new PromptTemplate("Hello, {name}!");
const prompt = template.format({ name: "World" });
awesome-gpt-prompt-engineering:
# Greeting Prompt
Input: {name}
Output: A friendly greeting
Prompt: Write a friendly greeting for {name}.
The code comparison shows that prompt-engine uses a more structured, programmatic approach with TypeScript, while awesome-gpt-prompt-engineering relies on markdown-based documentation for prompt templates.
Examples and guides for using the OpenAI API
Pros of openai-cookbook
- Comprehensive guide with practical examples and best practices
- Official resource from OpenAI, ensuring up-to-date and accurate information
- Covers a wide range of topics beyond prompt engineering, including API usage and model fine-tuning
Cons of openai-cookbook
- Focuses primarily on OpenAI's models and may not be as applicable to other language models
- Less community-driven content compared to awesome-gpt-prompt-engineering
- May not include as many creative or unconventional prompt engineering techniques
Code Comparison
openai-cookbook:
response = openai.Completion.create(
model="text-davinci-002",
prompt="Translate the following English text to French: '{}'",
temperature=0.3,
max_tokens=60
)
awesome-gpt-prompt-engineering:
Human: Translate the following English text to French:
"{text}"
Convert 
designs to code with AI
Introducing Visual Copilot: A new AI model to turn Figma designs to high quality code using your components.
Try Visual CopilotREADME
Awesome GPT Prompt Engineering 
A curated list of awesome resources, tools, and other shiny things for GPT prompt engineering.
Consider giving it a âï¸ if you like it to show your support!
ð RECOMMENDED: Use any LLM from the command line easily. ð
Table of Contents
Roadmaps
- Prompt Engineering Roadmap: Step by step guide to learning Prompt Engineering.
Guides
- Learn Prompt Engineering: Introduction to Prompt Engineering and Prompt Engineering techniques.
- Prompt Engineering Guide: Guides, papers, lecture, notebooks and resources for prompt engineering.
- Prompt Engineering 101: Prompt Engineering guide by Xavi.
- Prompt Engineering 101: Prompt Engineering guide by Raza Habib & Sinan Ozdemir.
- Prompt Engineering Guide: Prompt Engineering guide by Sudalai Rajkumar.
- How to generate text: using different decoding methods for language generation with Transformers: A guide to decoding methods for language generation with Transformers.
- The Illustrated Transformer: A visual guide to transformers, the core model used in GPT.
- Reddit's r/aipromptprogramming Tutorials Collection: A collection of tutorials for prompt engineering.
- Prompt Engineering Guide: A comprehensive guide that contains all the latest papers, learning resources, and developments in the field of prompt engineering.
- dair-ai/Prompt-Engineering-Guide: A GitHub repository that provides a prompt engineering guide with the latest papers and learning guides.
- How to Communicate with ChatGPT â A Guide to Prompt Engineering: A guide that explains what prompt engineering is and how you can use it to improve your communication with AI tools.
- A Beginner's Guide to ChatGPT Prompt Engineering: A beginner-friendly guide that delves into the art and science of Prompt Engineering.
- A Complete Introduction to Prompt Engineering for Large Language Models
- Prompt Engineering Guide: How to Engineer the Perfect Prompts
- Best practices for prompt engineering with OpenAI API: A guide by OpenAI that provides best practices for prompt engineering.
- ChatGPT Prompt Engineering for Developers: A short course on prompt engineering by deeplearning.ai.
- Natural Language Processing: Coursera specialization focusing on NLP.
- Learn Prompting: A Free, Open Source Course on Communicating with AI.
- Deep Learning Specialization: Coursera specialization by Andrew Ng, which includes a course on Sequence Models.
- OpenAI Cookbook: OpenAI's cookbook includes examples of prompt engineering.
- Tokens and Tokenization: Understanding Cost, Speed, and Limits with OpenAI's APIs: Everything tokens and tokenization. How to control costs/performance, how to handle Max Token limits, and a real-world example on how you can make your prompts more efficient.
- How OpenAI Parameters Actuallly Work: How to use OpenAI's parameters to experiment with prompts and get better outputs.
- A Beginner's Guide on Embeddings and Their Impact on Prompts: A Beginner's Guide on Embeddings and Their Impact on Prompts.
- Prompt Engineering for Vision Models: A beginner's guide to prompting vision models from DeepLearningAI.
Techniques
- Few Shot Learning: Everything you need to know about Few-Shot Learning.
- Zero Shot Learning: Large Language Models are Zero-Shot Reasoners.
- Chain of Thought: Encourages the LLM to explain its reasoning to improve its accuracy.
- Zero Shot Chain of Thought: Enable Chain of Thought with only a few words.
- Tree of Thoughts: Tree of Thoughts: Deliberate Problem Solving. with Large Language Models.
- Multi Persona Collaboration: Prompt the LLM to dynamically generate personas to collaborate to solve a task.
- Mastering ChatGPT Prompts: Mastering ChatGPT Prompts: Harnessing Zero, One, and Few-Shot Learning, Fine-Tuning, and Embeddings for Enhanced GPT Performance.
- Prompting GPT-3 To Be Reliable: Prompting GPT-3 To Be Reliable.
- Decomposed Prompting: A Modular Approach for Solving Complex Tasks.
- AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts.
- LangChain Github Repository: Building applications with LLMs through composability.
- Embedchain Github Repository: Framework to create ChatGPT-like bots over your dataset.
Prompt Collections
- FlowGPT: FlowGPT is the largest open source prompt community.
- awesomegptprompts.com: Explore hundreds of the best ChatGPT Prompts.
- fka/awesome-chatgpt-prompts: Dataset of awesome chatgpt prompts.
- f/awesome-chatgpt-prompts: This repo includes ChatGPT prompt curation to use ChatGPT better. .
- Awesome ChatGPT Prompts
- PromptHub
- ShowGPT.co
- Best Data Science ChatGPT Prompts
- ChatGPT prompts uploaded by the FlowGPT community
- Ignacio Velásquez Prompt Templates: 500+ ChatGPT Prompt Templates.
- PromptPal: A collection of prompts for GPT-3 and other language models.
- Hero GPT: AI Prompt Library.
- Reddit's ChatGPT Prompts
- Snack Prompt: GPT prompts collection, has a a Chrome extension.
- ShareGPT: Share your prompts and your entire conversations.
- Prompt Search: a search engine for AI Prompts.
- PromptBase: The largest prompts marketplace on the web.
- The Ultimate 5 ChatGPT Prompts: Simplify Your AI Experience.
- The Prompt Index: A vast collection of carefully curated prompts, stimulating imagination and fueling creative endeavours.
- PromptDen: A growing list of thousands of prompts for both text and image generation. Free to explore, add your own, save your favorites and even create a profile page for prompt engineering.
Papers
- Attention Is All You Need: Transformer introduction paper.
- Language Models are Few-Shot Learners: GPT-3 introduction paper by OpenAI.
- Fine-Tuning Language Models from Human Preferences: Important paper on fine-tuning language models by OpenAI.
- The Power of Scale for Parameter-Efficient Prompt Tuning: Explores the benefits of "prompt tuning" for robust task performance.
- Deep Attentive Learning for Stock Movement Prediction From Social Media Text and Company Correlations: Introduces an architecture for accurate stock forecasting using financial data and social media signals.
- A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT
- Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery.
- Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models.
- Progressive Prompts: Continual Learning for Language Models.
- Batch Prompting: Efficient Inference with LLM APIs.
- Successive Prompting for Decompleting Complex Questions
- Structured Prompting: Scaling In-Context Learning to 1,000 Examples.
- Large Language Models Are Human-Level Prompt Engineers
- Ask Me Anything: A simple strategy for prompting language models.
- PromptChainer: Chaining Large Language Model Prompts through Visual Programming.
- Reframing Instructional Prompts to GPTk's Language
- Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm.
- Prefix-Tuning: Optimizing Continuous Prompts for Generation
- Multimodal Chain-of-Thought Reasoning in Language Models
- On Second Thought, Let's Not Think Step by Step!: Bias and Toxicity in Zero-Shot Reasoning.
- ReAct: Synergizing Reasoning and Acting in Language Models
- Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought.
- On the Advance of Making Language Models Better Reasoners
- Large Language Models are Zero-Shot Reasoners
- Reasoning Like Program Executors
- Self-Consistency Improves Chain of Thought Reasoning in Language Models
- Chain of Thought Prompting Elicits Reasoning in Large Language Models
- Generated Knowledge Prompting for Commonsense Reasoning
- Large Language Models Can Be Easily Distracted by Irrelevant Context
- Constitutional AI: Harmlessness from AI Feedback
Books
- The ChatGPT Prompt Book: A book dedicated to ChatGPT prompts.
- You Look Like a Thing and I Love You: A book about AI with a focus on language models.
Communities
- OpenAI Discord Server: The official OpenAI Discord server.
- Attention Architects: Prompt Engineering expert & open source community.
- ChatGPT Prompt Engineering Discord Server: A Discord server dedicated to prompt engineering.
- Attention Architects: Prompt Engineering open source community.
- r/MachineLearning: The Machine Learning subreddit often has discussions on GPT and other language models.
- Hugging Face Forum: A forum for discussing Hugging Face's transformer models, including GPT.
- ChatGPT Community Discord Server: A Discord server dedicated to ChatGPT.
- Reddit's ChatGPT Discord Server: r/chatgpt Discord server.
- PromptsLab Discord: Knowledge sharing community for Generative Models, Prompt Engineering, LLMs.
- Learn Prompting: A Discord server dedicated to learning about prompts.
- Artificial Intelligence Discord: Discord server for AI enthusiasts and prompt engineers.
Playgrounds and Alternative UIs
- Official OpenAI Playground
- llm: Use any LLM from the command line, easily.
- Nat.Dev: Multiple Chat AI Playground & Comparer.
- Poe.com: All in one playground: GPT4, Sage, Claude+, Dragonfly, and more...
- Ora.sh GPT-4 Chatbots
- Better ChatGPT: A web app with a better UI for exploring OpenAI's ChatGPT API.
- LMQL.AI: A programming language and platform for language models.
- Vercel Ai Playground: One prompt, multiple Models (including GPT-4).
- Conju.ai: A visual prompt chaining app.
- Voiceflow: Professional collaborative visual prompt-chaining tool.
- Opik: Evaluate, test, and ship LLM applications across your dev and production lifecycles.
Prompt Generators
- Promptify: Automatically improve your prompt.
- Fusion: Elevate your output with Fusion's smart prompts.
- Bumble-Prompts: Let AI Write your bumble prompt.
- ChatGPT Prompt Generator: Generates ChatGPT prompts based on a BART model.
- PromptPerfect: Prompt optimizer.
- Hero GPT: AI Prompt Generator.
- LMQL: Query language for programming large language models.
- OpenPromptStudio
- BossGPT
Auto-GPT Related
- Auto-GPT Official Repo
- Auto-GPT God Mode
- OpenAIMaster's Guide to Auto-GPT: How does Auto-GPT work, an AI tool to create full projects.
- AgentGPT: GPT agents in browser.
- DemoGPT: 𧩠DemoGPT enables you to create quick demos by just using prompts.
Prompt Injection
- Understanding Prompt Injections and What You Can Do About Them: An introduction to prompt injections with examples and tactics you can use to mitigate potential risks in your application.
- Learn Prompting's Prompt Injection guide: A guide to prompt injections with examples.
- Prompt injection: What's the worst that can happen?
- Prompt injections are bad, mkay?
ChatGPT Plug-ins
- ChatGPT plugins: OpenAI Official Page.
- Plug-in example code in Python: Example code for creating a ChatGPT plug-in in Python.
- Surfer Plug-in source code
- Security: (PAID) Create, deploy, monitor and secure LLM Plugins.
Prompt Engineering Jobs Offers
- Prompt-Talent: Prompt engineering job offers.
AI Links Directories
- llm: Use any LLM from the command line.
- FuturePedia: The Largest AI Tools Directory Updated Daily.
- Theresanaiforthat: The biggest AI aggregator.
- Awesome-Prompt-Engineering
- AiTreasureBox
- EwingYangs Awesome-open-gpt
- KennethanCeyer Awesome-llmops
- KennethanCeyer awesome-llm
- tensorchord Awesome-LLMOps
Contributing
Contributions are always welcome! Please read the contribution guidelines first.
How to help:
- Give a âï¸ to increase the repository's visibility.
- Add descriptions for resources that don't have them.
- Add new resources to the list.
- Fix typos or grammatical errors.
- Share this repository with others.
Featured
ð RECOMMENDED: Use any LLM from the command line easily with llm. ð
Top Related Projects
This repo includes ChatGPT prompt curation to use ChatGPT better.
🐙 Guides, papers, lecture, notebooks and resources for prompt engineering
This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc
A library for helping developers craft prompts for Large Language Models
Examples and guides for using the OpenAI API
Convert designs to code with AI
Introducing Visual Copilot: A new AI model to turn Figma designs to high quality code using your components.
Try Visual Copilot