Prompt Engineering Guide

Prompt engineering is a relatively new discipline for developing and optimizing prompts to efficiently use language models (LMs) for a wide variety of applications and research topics. Prompt engineering skills help to better understand the capabilities and limitations of large language models (LLMs).

Researchers use prompt engineering to improve the capacity of LLMs on a wide range of common and complex tasks such as question answering and arithmetic reasoning. Developers use prompt engineering to design robust and effective prompting techniques that interface with LLMs and other tools.

Prompt engineering is not just about designing and developing prompts. It encompasses a wide range of skills and techniques that are useful for interacting and developing with LLMs. It's an important skill to interface, build with, and understand capabilities of LLMs. You can use prompt engineering to improve safety of LLMs and build new capabilities like augmenting LLMs with domain knowledge and external tools.

Motivated by the high interest in developing with LLMs, we have created this new prompt engineering guide that contains all the latest papers, learning guides, models, lectures, references, new LLM capabilities, and tools related to prompt engineering.

• Prompt Engineering
• Introduction
  • LLM Settings
  • Basics of Prompting
  • Prompt Elements
  • General Tips for Designing Prompts
  • Examples of Prompts
• Techniques
  • Zero-shot Prompting
  • Few-shot Prompting
  • Chain-of-Thought Prompting
  • Self-Consistency
  • Generate Knowledge Prompting
  • Automatic Prompt Engineer
  • Active-Prompt
  • Directional Stimulus Prompting
  • ReAct
  • Multimodal CoT
  • Graph Prompting
• Applications
  • Program-Aided Language Models
  • Generating Data
  • Graduate Job Classification Case Study
• Models
  • Flan
  • ChatGPT
  • LLaMA
  • GPT-4
  • Model Collection
• Risks & Misuses
  • Adversarial Prompting
  • Factuality
  • Biases
• Papers
• Tools
• Notebooks
• Datasets
• Additional Readings