Awesome GPT Prompt Engineering

Overview

Awesome GPT Prompt Engineering is the largest open-source prompt community, featuring comprehensive resources for mastering prompt engineering with GPT models and other LLMs.

Prompt Engineering Fundamentals

Core Concepts

• Zero-shot Prompting: Getting results without examples
• Few-shot Learning: Providing examples in the prompt
• Chain-of-Thought (CoT): Step-by-step reasoning
• Tree of Thoughts: Exploring multiple reasoning paths
• Self-Consistency: Sampling multiple outputs for consensus
• Role Prompting: Assigning personas to the AI

Advanced Techniques

• Prompt Chaining: Breaking complex tasks into steps
• ReAct: Reasoning and Acting in language models
• Constitutional AI: Harmlessness through prompting
• Retrieval-Augmented Generation (RAG): Combining prompts with external knowledge
• Least-to-Most Prompting: Progressive problem decomposition

Prompt Libraries and Collections

Major Repositories

• FlowGPT: Large community prompt sharing platform
• PromptBase: Marketplace for buying/selling prompts
• ShareGPT: Community-curated conversations
• Awesome ChatGPT Prompts: Popular prompt templates
• LangChain Hub: Prompt templates for LangChain

Specialized Collections

• Programming prompts: Code generation and debugging
• Writing prompts: Content creation and editing
• Analysis prompts: Data analysis and interpretation
• Creative prompts: Story writing, brainstorming
• Business prompts: Marketing, strategy, planning

Tools and Platforms

Prompt Development

• LangChain: Framework for LLM application development
• LlamaIndex: Data framework for LLM applications
• Guidance: Programming language for controlling LLMs
• Semantic Kernel: Microsoft's SDK for AI orchestration
• Promptify: Structured prompt generation library

Testing and Optimization

• PromptPerfect: Automated prompt optimization
• Promptfoo: Test harness for LLM prompts
• OpenAI Evals: Evaluation framework for LLM outputs
• LangSmith: Debugging and testing for LLM apps

Prompt IDEs

• PromptLayer: Prompt engineering platform with tracking
• Dyno: Visual prompt engineering tool
• PromptHub: Collaborative prompt management
• Humanloop: Prompt experimentation and deployment

Use Cases and Applications

Content Creation

• Blog Writing: Article outlines, drafts, editing
• Social Media: Post ideas, captions, engagement
• Email: Professional emails, marketing copy
• Documentation: Technical writing, user guides

Code and Development

• Code Generation: Writing functions, classes, scripts
• Debugging: Finding and fixing bugs
• Code Review: Analyzing and improving code
• Documentation: Generating comments and docs
• Testing: Creating test cases

Data and Analysis

• Data Extraction: Structured data from text
• Summarization: Condensing long documents
• Classification: Categorizing content
• Sentiment Analysis: Understanding tone and emotion
• Q&A Systems: Question answering from documents

Business Applications

• Customer Support: Automated responses, FAQs
• Market Research: Competitive analysis, trend insights
• Sales: Pitch decks, proposal writing
• HR: Job descriptions, interview questions

Best Practices

Prompt Design Principles

• Be Specific: Clear, detailed instructions
• Provide Context: Background information
• Use Examples: Show desired output format
• Set Constraints: Length, format, style requirements
• Iterate: Refine based on results
• Test Variations: A/B test different approaches

Common Patterns

• Instruction + Input + Output Format: Standard template
• Role + Task + Context + Examples: Comprehensive structure
• CRISPE Framework: Capacity, Role, Insight, Statement, Personality, Experiment
• RISEN Framework: Role, Instructions, Steps, End goal, Narrowing

Model-Specific Techniques

GPT-4 and GPT-3.5

• System messages for behavior guidance
• Function calling for structured outputs
• JSON mode for reliable formatting
• Vision capabilities (GPT-4V)
• Extended context windows (128K tokens)

Claude (Anthropic)

• Constitutional AI principles
• Longer context windows (200K tokens)
• XML tag formatting
• Think step-by-step prompting

Open-Source Models

• Llama 2/3 specific prompts
• Mistral formatting
• Alpaca instruction format
• Vicuna conversation templates

Research and Papers

Foundational Papers

• "Chain-of-Thought Prompting Elicits Reasoning": Original CoT paper
• "Large Language Models are Zero-Shot Reasoners": "Let's think step by step"
• "Tree of Thoughts": Deliberate problem solving
• "ReAct": Synergizing reasoning and acting
• "Self-Consistency": Improved reasoning through sampling

Courses and Learning

Online Courses

• DeepLearning.AI: ChatGPT Prompt Engineering for Developers
• OpenAI Cookbook: Official examples and guides
• Anthropic Prompt Engineering Guide: Claude-specific techniques
• LangChain Academy: Comprehensive LLM development

Community Resources

• Learn Prompting: Free comprehensive course
• Prompt Engineering Guide (DAIR.AI): Research-backed guide
• OpenAI Documentation: Official best practices

Prompt Engineering for Specific Domains

Healthcare

• Medical diagnosis assistance
• Patient communication
• Clinical documentation
• Research literature review

Legal

• Contract analysis
• Legal research
• Document drafting
• Case summarization

Education

• Lesson planning
• Student feedback
• Quiz generation
• Personalized tutoring

Finance

• Financial analysis
• Report generation
• Risk assessment
• Market research

Safety and Ethics

• Jailbreak Prevention: Protecting against prompt injection
• Bias Mitigation: Addressing model biases
• Privacy Concerns: Handling sensitive data
• Hallucination Detection: Verifying factual accuracy
• Content Filtering: Ensuring appropriate outputs

Community and Forums

• r/PromptEngineering: Reddit community
• Discord servers: LangChain, OpenAI, various AI communities
• Twitter/X: #PromptEngineering hashtag
• LinkedIn groups: Professional prompt engineering

Pricing

Free and open-source repository. Linked tools and platforms range from free to enterprise pricing.