Original Article: Prompt Engineering: Getting What You Want From ChatGPT Using CoT and Synthetic Prompts July 26, 2023 By Rick Hightower

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LLM Prompt Engineering with Chain of Thought and Synthetic Prompts: Getting what you want from ChatGPT using CoT Part 2

Introduction

Large Language Models (LLMs) have become increasingly popular in recent years due to their ability to understand and generate human-like text across various domains (V7 Labs) (Scale AI). These models, such as OpenAI's GPT-4 and Google's PaLM 2, are trained on massive amounts of text data, allowing them to excel at tasks like text generation, summarization, translation, and sentiment analysis (Scale AI) (Moveworks). However, despite their impressive capabilities, LLMs must improve their reasoning and problem-solving tasks (Moveworks) to solve complex problems.

In this article, we will explore the concept of Chain-of-Thought (CoT) prompting, a technique designed to enhance LLMs' reasoning abilities. We will discuss the implementation of CoT prompting, its applications, and how it can improve the performance of LLMs in various tasks. By understanding the potential of CoT prompting, we can unlock new possibilities for LLM development and application, ultimately pushing the boundaries of artificial intelligence. This is a follow-up to the first article, where we focused on few-shot CoT vs. single-shot prompts and prompts with light prompt engineering.

Topics covered

Chain-of-Thought Prompting

• Definition and purpose of Chain-of-Thought (CoT) prompting
• How CoT prompting enhances LLMs' reasoning capabilities (KDnuggets)
• Decomposition of multi-step requests into intermediate steps (Cobus Greyling)

Strategies for CoT Prompting

• Few-shot CoT: Providing examples of Question-Answer pairs with step-by-step explanations
• Zero-shot CoT: Prefixing the Answer block with "Let's think step by step" to prompt the LLM to complete the output in that format

Implementing Chain-of-Thought Prompting

• Prompt engineering techniques for establishing chain-of-thought reasoning (Cobus Greyling)
• Comparison of standard LLM prompting and chain-of-thought prompting (Cobus Greyling)
• The benefits of decomposition for input and output interpretation (Cobus Greyling)