https://media.licdn.com/dms/image/v2/D5612AQF7vJDJtDzpVw/article-cover_image-shrink_720_1280/article-cover_image-shrink_720_1280/0/1690825545417?e=1736380800&v=beta&t=66qZTNaNKKQPKf1xV7nVH5NGpTsPt3ylcgdbf6P0pIY
Author: Rick Hightower
We implement a real-world use case that most developers and tech managers should understand. We give ChatGPT a Java method and ask it to produce a Mermaid markdown format.
Chain of Thought (CoT) prompting is a technique that improves the performance of Large Language Models (LLMs) on reasoning-based tasks through few-shot learning. According to Toward Data Science, CoT enables LLMs to address complex tasks, such as common sense reasoning and arithmetic, by breaking down multi-step requests into intermediate steps. This decomposition creates a window of insight and interpretation, allowing for manageable granularity for both input and output, making it easier to tweak the system.
CoT prompting breaks down a problem into a series of intermediate reasoning steps, thus significantly improving the ability of LLMs to perform complex reasoning. There are different strategies for implementing CoT prompting, such as few-shot CoT and zero-shot CoT. In few-shot CoT, examples of Question-Answer pairs are provided where the answer is explained step by step. In zero-shot CoT, the Answer block is prefixed with "Let's think step by step" to prompt the LLM to complete the output in that format. The benefits of CoT prompting become more apparent as the model scale increases, leading to improved performance that substantially outperforms standard prompting for large model sizes. These findings are supported by experiments on three large language models, as described in Google AI Blog and arXiv.
ChatGPT is an AI language model that generates human-like text and engages in conversations. It's like an intelligent computer program that can understand and generate text based on context. However, it's important to remember that ChatGPT is not perfect and can sometimes forget or misunderstand the context, especially if the conversation is long or complex. To help ChatGPT better understand and remember the context, you can try the following:
ChatGPT is like predictive text, so it's essential to prime its context to produce the desired output. By following these tips, you can help improve ChatGPT's understanding of the context and increase the chances of it generating the text you want. Let’s break down what COT is and then show an example. This article focuses on CoT.
Chain of Thought (CoT) prompting is a technique that improves the performance of Large Language Models (LLMs) on reasoning-based tasks through few-shot learning. According to Toward Data Science, CoT enables LLMs to address complex tasks, such as common sense reasoning and arithmetic, by breaking down multi-step requests into intermediate steps. This decomposition creates a window of insight and interpretation, allowing for manageable granularity for both input and output, making it easier to tweak the system.
CoT prompting breaks a problem down into a series of intermediate reasoning steps, thus significantly improving the ability of LLMs to perform complex reasoning. There are different strategies for implementing CoT prompting, such as few-shot CoT and zero-shot CoT. In few-shot CoT, examples of Question-Answer pairs are provided where the answer is explained step by step. In zero-shot CoT, the Answer block is prefixed with "Let's think step by step" to prompt the LLM to complete the output in that format. The benefits of CoT prompting become more apparent as the model scale increases, leading to improved performance that substantially outperforms standard prompting for large model sizes. These findings are supported by experiments on three large language models described in Google AI Blog and arXiv.