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πŸ˜ƒ Basics
🧠 Advanced
Zero-Shot
🟒 Introduction
🟒 Emotion Prompting
🟒 Role Prompting
🟒 Re-reading (RE2)
🟒 Rephrase and Respond (RaR)
🟦 SimToM
β—† System 2 Attention (S2A)
Few-Shot
🟒 Introduction
🟒 Self-Ask
🟒 Self Generated In-Context Learning (SG-ICL)
🟒 Chain-of-Dictionary (CoD)
🟒 Cue-CoT
🟦 Chain of Knowledge (CoK)
β—† K-Nearest Neighbor (KNN)
β—†β—† Vote-K
β—†β—† Prompt Mining
Thought Generation
🟒 Introduction
🟒 Chain of Draft (CoD)
🟦 Contrastive Chain-of-Thought
🟦 Automatic Chain of Thought (Auto-CoT)
🟦 Tabular Chain-of-Thought (Tab-CoT)
🟦 Memory-of-Thought (MoT)
🟦 Active Prompting
🟦 Analogical Prompting
🟦 Complexity-Based Prompting
🟦 Step-Back Prompting
🟦 Thread of Thought (ThoT)
Ensembling
🟒 Introduction
🟒 Universal Self-Consistency
🟦 Mixture of Reasoning Experts (MoRE)
🟦 Max Mutual Information (MMI) Method
🟦 Prompt Paraphrasing
🟦 DiVeRSe (Diverse Verifier on Reasoning Step)
🟦 Universal Self-Adaptive Prompting (USP)
🟦 Consistency-based Self-adaptive Prompting (COSP)
🟦 Multi-Chain Reasoning (MCR)
Self-Criticism
🟒 Introduction
🟒 Self-Calibration
🟒 Chain of Density (CoD)
🟒 Chain-of-Verification (CoVe)
🟦 Self-Refine
🟦 Cumulative Reasoning
🟦 Reversing Chain-of-Thought (RCoT)
β—† Self-Verification
Decomposition
🟒 Introduction
🟒 Chain-of-Logic
🟦 Decomposed Prompting
🟦 Plan-and-Solve Prompting
🟦 Program of Thoughts
🟦 Tree of Thoughts
🟦 Chain of Code (CoC)
🟦 Duty-Distinct Chain-of-Thought (DDCoT)
β—† Faithful Chain-of-Thought
β—† Recursion of Thought
β—† Skeleton-of-Thought
πŸ”“ Prompt Hacking
🟒 Defensive Measures
🟒 Introduction
🟒 Filtering
🟒 Instruction Defense
🟒 Post-Prompting
🟒 Random Sequence Enclosure
🟒 Sandwich Defense
🟒 XML Tagging
🟒 Separate LLM Evaluation
🟒 Other Approaches
🟒 Offensive Measures
🟒 Introduction
🟒 Simple Instruction Attack
🟒 Context Ignoring Attack
🟒 Compound Instruction Attack
🟒 Special Case Attack
🟒 Few-Shot Attack
🟒 Refusal Suppression
🟒 Context Switching Attack
🟒 Obfuscation/Token Smuggling
🟒 Task Deflection Attack
🟒 Payload Splitting
🟒 Defined Dictionary Attack
🟒 Indirect Injection
🟒 Recursive Injection
🟒 Code Injection
🟒 Virtualization
🟒 Pretending
🟒 Alignment Hacking
🟒 Authorized User
🟒 DAN (Do Anything Now)
🟒 Bad Chain
πŸ”¨ Tooling
Prompt Engineering IDEs
🟒 Introduction
GPT-3 Playground
Dust
Soaked
Everyprompt
Prompt IDE
PromptTools
PromptSource
PromptChainer
Prompts.ai
Snorkel 🚧
Human Loop
Spellbook 🚧
Kolla Prompt 🚧
Lang Chain
OpenPrompt
OpenAI DALLE IDE
Dream Studio
Patience
Promptmetheus
PromptSandbox.io
The Forge AI
AnySolve
Conclusion
πŸ”¨ ToolingPrompt Engineering IDEsOpenPrompt

OpenPrompt

Reading Time: 1 minute
Last updated on August 7, 2024

Sander Schulhoff

OpenPrompt is a Python library that provides various functionality for building and testing prompts.

Features

OpenPrompt supports models from huggingface, but not yet OpenAI. It focuses more on datasets and testing than chaining. OpenPrompt also appears to support soft-prompting via https://github.com/thunlp/OpenDelta.

Sander Schulhoff

Sander Schulhoff is the CEO of HackAPrompt and Learn Prompting. He created the first Prompt Engineering guide on the internet, two months before ChatGPT was released, which has taught 3 million people how to prompt ChatGPT. He also partnered with OpenAI to run the first AI Red Teaming competition, HackAPrompt, which was 2x larger than the White House's subsequent AI Red Teaming competition. Today, HackAPrompt partners with the Frontier AI labs to produce research that makes their models more secure. Sander's background is in Natural Language Processing and deep reinforcement learning. He recently led the team behind The Prompt Report, the most comprehensive study of prompt engineering ever done. This 76-page survey, co-authored with OpenAI, Microsoft, Google, Princeton, Stanford, and other leading institutions, analyzed 1,500+ academic papers and covered 200+ prompting techniques.