A comprehensive collection of research papers, tools, tutorials, and community resources to advance your prompt engineering skills
Prompt Engineering Guides
Free, comprehensive prompt engineering documentation directly from leading AI companies
OpenAI Prompt Engineering Guide
Provider: OpenAIBest For: ChatGPT users
Anthropic Prompt Engineering Guide
Provider: AnthropicBest For: Claude users
Google AI Prompt Design Guide
Provider: Google AIBest For: Gemini users
Microsoft Copilot Prompt Guide
Provider: Microsoft AzureBest For: ChatGPT users
AWS Bedrock Prompt Engineering
Provider: Amazon Web ServicesBest For: Developers
Cohere Prompt Engineering
Provider: CohereBest For: Cohere users
Prompt Libraries & Development
Potentially
Purpose: Marketplace for promptsFeatures:
- Pre-built prompts
- Community sharing
- Prompt templates
LangChain
Purpose: Framework for building LLM applicationsFeatures:
- Chain composition
- Memory management
- Tool integration
LlamaIndex
Purpose: Data framework for LLM applicationsFeatures:
- Data ingestion and indexing
- RAG implementation
- Query engines
OpenAI Playground
Purpose: Interactive prompt testingFeatures:
- Real-time testing
- Parameter tuning
- Model comparison
Foundational Research Papers
Language Models & Transformers
Attention Is All You Need (2017)
Attention Is All You Need (2017)
Authors: Vaswani et al., Google BrainKey Contribution: Introduced the Transformer architecture that powers modern LLMsWhy Read: Understanding transformers is fundamental to understanding how prompts are processedLink: arXiv:1706.03762
Language Models are Few-Shot Learners (2020)
Language Models are Few-Shot Learners (2020)
Authors: Brown et al., OpenAI (GPT-3 Paper)Key Contribution: Demonstrated that large language models can perform tasks with just a few examples (few-shot learning)Why Read: Foundational paper on in-context learning and prompt-based task solvingLink: arXiv:2005.14165
Training language models to follow instructions (2022)
Training language models to follow instructions (2022)
Authors: Ouyang et al., OpenAI (InstructGPT Paper)Key Contribution: Showed how RLHF (Reinforcement Learning from Human Feedback) improves instruction followingWhy Read: Explains why modern models are better at following promptsLink: arXiv:2203.02155
Prompting Techniques
Chain of Thought & Reasoning
Chain-of-Thought Prompting Elicits Reasoning (2022)
Chain-of-Thought Prompting Elicits Reasoning (2022)
Authors: Wei et al., Google ResearchKey Contribution: Introduced Chain of Thought prompting, showing 30-50% accuracy improvements on reasoning tasksWhy Read: The definitive paper on CoT promptingLink: arXiv:2201.11903
Large Language Models are Zero-Shot Reasoners (2022)
Large Language Models are Zero-Shot Reasoners (2022)
Authors: Kojima et al., University of TokyoKey Contribution: Showed that simply adding “Let’s think step by step” dramatically improves reasoningWhy Read: Demonstrates the power of simple prompting modificationsLink: arXiv:2205.11916
Self-Consistency Improves Chain of Thought Reasoning (2022)
Self-Consistency Improves Chain of Thought Reasoning (2022)
Authors: Wang et al., Google ResearchKey Contribution: Introduced self-consistency (ensembling multiple reasoning paths)Why Read: Shows how to improve CoT reliability through multiple samplesLink: arXiv:2203.11171
Tree of Thoughts: Deliberate Problem Solving (2023)
Tree of Thoughts: Deliberate Problem Solving (2023)
Authors: Yao et al., Princeton UniversityKey Contribution: Extended CoT to explore multiple reasoning branches like a search treeWhy Read: Advanced technique for complex problem-solvingLink: arXiv:2305.10601
Retrieval-Augmented Generation
Retrieval-Augmented Generation for Knowledge-Intensive NLP (2020)
Retrieval-Augmented Generation for Knowledge-Intensive NLP (2020)
Authors: Lewis et al., Facebook AI ResearchKey Contribution: Introduced RAG, combining retrieval with generationWhy Read: Foundational paper on grounding LLM outputs in external knowledgeLink: arXiv:2005.11401
In-Context Retrieval-Augmented Language Models (2023)
In-Context Retrieval-Augmented Language Models (2023)
Authors: Ram et al., AI21 LabsKey Contribution: Showed how to effectively integrate retrieved documents into promptsWhy Read: Practical techniques for implementing RAGLink: arXiv:2302.00083
Prompt Engineering Surveys
Pre-train, Prompt, and Predict (2021)
Pre-train, Prompt, and Predict (2021)
Authors: Liu et al., Carnegie Mellon UniversityKey Contribution: Comprehensive survey of prompting methodsWhy Read: Excellent overview of the prompting landscapeLink: arXiv:2107.13586
A Survey of Large Language Models (2023)
A Survey of Large Language Models (2023)
Authors: Zhao et al., Renmin University of ChinaKey Contribution: Comprehensive survey covering LLM architectures, training, and promptingWhy Read: Up-to-date overview of the entire LLM fieldLink: arXiv:2303.18223
Tools & Frameworks
Vector Databases (for RAG)
Pinecone
Managed vector database with high performance
Weaviate
Open-source vector search engine
Chroma
Lightweight, embeddable vector database
Qdrant
High-performance vector similarity search
Evaluation & Testing
PromptFoo
Purpose: Test and evaluate promptsFeatures:
- Automated testing
- Performance metrics
- Regression detection
LangSmith
Purpose: Debug and monitor LLM applicationsFeatures:
- Trace visualization
- Performance analytics
- Prompt versioning
Community Resources
Learning Platforms
Learn Prompting
Free, comprehensive prompt engineering course
Prompt Engineering Guide
Open-source guide with examples and techniques
OpenAI Cookbook
Official examples and best practices from OpenAI
Anthropic Prompt Library
Curated prompts for Claude
Blogs & Newsletters
Lilian Weng's Blog
Lilian Weng's Blog
Focus: Deep dives into LLM research and techniquesNotable Posts:
- “Prompt Engineering”
- “Large Language Models”
- “Controllable Text Generation”
The Batch (DeepLearning.AI)
The Batch (DeepLearning.AI)
Focus: Weekly AI news and insightsWhy Subscribe: Stay current with AI developmentsLink: deeplearning.ai/the-batch
Import AI
Import AI
Focus: Weekly newsletter on AI researchWhy Subscribe: Curated research paper summariesLink: jack-clark.net
Ahead of AI
Ahead of AI
Focus: Practical AI and prompt engineeringWhy Subscribe: Actionable tips and techniquesLink: magazine.sebastianraschka.com
Communities
Prompt University Discord
Education Community for Prompt University
r/PromptEngineering
Active Reddit community for prompt engineering discussions
OpenAI Forum
Official OpenAI community forum
Hugging Face Forums
Community for open-source AI models
Books
Recommended Reading
The Prompt Engineering Handbook
The Prompt Engineering Handbook
Authors: Various contributorsFocus: Comprehensive guide to prompt engineeringBest For: Structured learning pathAvailability: Free online
Designing Machine Learning Systems
Designing Machine Learning Systems
Author: Chip HuyenFocus: Production ML systems (includes prompting)Best For: Building real-world applicationsPublisher: O’Reilly Media
Natural Language Processing with Transformers
Natural Language Processing with Transformers
Authors: Tunstall, von Werra, WolfFocus: Deep dive into transformer modelsBest For: Understanding the underlying technologyPublisher: O’Reilly Media
Online Courses
Structured Learning
ChatGPT Prompt Engineering for Developers
Provider: DeepLearning.AI + OpenAIDuration: 1 hourLevel: BeginnerCost: Free
LangChain for LLM Application Development
Provider: DeepLearning.AIDuration: 1 hourLevel: IntermediateCost: Free
Building Systems with ChatGPT API
Provider: DeepLearning.AI + OpenAIDuration: 1 hourLevel: IntermediateCost: Free
Prompt Engineering Specialization
Provider: Vanderbilt University (Coursera)Duration: 4 coursesLevel: Beginner to AdvancedCost: Paid (with free audit option)
Advanced Topics
Cutting-Edge Research Areas
- Constitutional AI
- Multimodal Prompting
- Prompt Optimization
- Adversarial Prompting
Focus: Training AI systems to be helpful, harmless, and honestKey Paper: “Constitutional AI: Harmlessness from AI Feedback” (Anthropic, 2022)Why Important: Addresses AI safety and alignmentLink: arXiv:2212.08073
Research Groups & Labs
Leading Organizations
OpenAI
GPT series, DALL-E, ChatGPT
Anthropic
Claude, Constitutional AI
Google DeepMind
PaLM, Gemini, Flamingo
Meta AI
LLaMA, RAG research
Microsoft Research
Orca, Phi models
Stanford NLP
Academic research on LLMs
Staying Current
How to Keep Up
1
Follow Key Researchers
Twitter/X accounts: @AndrewYNg, @karpathy, @ylecun, @goodfellow_ian, @sama
2
Monitor arXiv
Subscribe to cs.CL (Computation and Language) and cs.AI categories
3
Join Communities
Participate in Discord servers, Reddit, and forums
4
Experiment Regularly
Try new techniques as they’re published
5
Read Release Notes
Follow model updates from OpenAI, Anthropic, Google, etc.
Practice Resources
Datasets for Practice
MMLU
Massive Multitask Language Understanding - 57 subjects
BIG-bench
Beyond the Imitation Game - 200+ diverse tasks
HELM
Holistic Evaluation of Language Models
PromptSource
Collection of prompts for NLP datasets
Prompt University Courses
Glossary
Key terms and definitions
Best Practices
Quick reference guide
Module 1
Prompt Engineering Fundamentals
Module 2
Task-Specific Prompting
Module 3
Advanced Techniques
Note: This field evolves rapidly. Links and resources are current as of 2025, but new papers and tools emerge frequently. Check the communities and newsletters above to stay updated.