What Is Chain of Thought Prompting and How to Use It

Chain of thought prompting is a tool focused on enhancing language models' performance. Technologies like virtual assistants and chatbots can benefit from it by giving more accurate answers when performing tasks that require logic, calculation, and decision-making.

With the chain of thought prompting it's possible to mimic human reasoning, improving language models. Check out the article to learn more about it.

What Is Chain of Thought Prompting?#

Chain or thought prompting is a technique that helps natural language processing (NLP) to improve logic and reasoning skills to become as close as possible to human reasoning. Large language models (LLMs) commonly struggle with complex reasoning tasks, the chain of thought prompting will guide it step-by-step through the reasoning process.

The AI developer will provide examples showing how to solve a problem using logic. This way, building more reliable and trustworthy AI systems is possible as you can break down the path for each answer. The AI assistants and chatbots become able to give clearer explanations.

The basis of the chain of thought prompting is the belief that it's easier to deal with complex problems by breaking them down into smaller pieces. Instead of jumping to conclusions, the AI is taught to consider different aspects of the problem before forming an answer.

The concept of chain of thought prompting is new. It's mostly guided by the Seminal Work report made in 2022 by Google Researchers. They proved how using the chain of thought prompting is more effective in solving math problems, common sense, and symbolic reasoning.

How Chain of Thought Prompting Works#

The chain of thought prompting works by asking LLMs to mimic the human process of decomposing a problem and working it through step-by-step. For example, when someone solves a complex math equation, they usually need to divide it into smaller parts to solve the equation and get the correct result.

This can be done using different strategies such as:

Explicit Instructions#

In this case, the problem is presented to the AI system and decomposed into smaller pieces. This way the AI can understand how reasoning and logic processes work to repeat them. For instance, it uses sentences like "First, we need to consider…".

For example, if you want the coffee-related words in English from the text below, you should explain what the AI needs to do step-by-step.

Source: DataCamp

Source: DataCamp

Implicit Instructions#

In some situations, it isn't necessary to explain the whole process to the AI. After the prompt, you can use a simple command such as "Let’s think step by step". This model is called Zero-Shot Chain-of-Thought.

In the example below, the author asks the AI to solve an arithmetic problem, but it isn't able to deliver an answer. In the second case, when the author adds "Let's think step-by-step, the AI can mimic the human logic process, and give the correct answer.

Source: Large Language Models are Zero-Shot Reasoners

Demonstrative Examples#

In this case, the AI is given demonstrative examples of how the reasoning process works. The model can see step-by-step how the problem is solved, and repeat the logic used to solve other problems.

Chain of Thought Prompting in Practice#

Arithmetic Reasoning#

The change of thought prompting can be used to fix errors made by a large language model when solving arithmetic problems. It does that by explaining the arithmetic reasoning process and the steps involved.

In the example below, the developer explains how the subtraction process must be done. If any error is detected a new prompt is created to guide the LLM.

Source: ClickUp

Commonsense Reasoning#

Chain of thought prompting is also used to guide AI systems to understand cause-and-effect relationships, so it becomes able to make commonsense conclusions.

Sentiment Analysis#

In sentiment analysis, the chain of thought prompting is used to enable AI coding models to interpret complex sentences. The LLM identifies words that express sentiment, and analyzes them.

Source: ClickUp

Customer Service Chatbots#

Some chatbots use the chain of thought to understand customer queries better and provide accurate answers to assist them. The customer's problem is broken down into smaller, manageable parts, which allows the chatbots to analyze it better, reducing the need for human intervention.

Education and Learning#

Chain of thought prompting is very useful for educational technology platforms as it enables them to offer step-by-step explanations of complex issues.

It's a valuable asset in subjects like science and math, where it's necessary to understand the path that leads to the final answer. The AI system will guide students through every step, helping them to solve problems.

Benefits of Chain of Thought Prompting#

Accuracy#

With the chain of thought prompting, AI tools can offer more accurate answers. As the problem is solved by breaking it down into smaller parts, it's possible to check if there is any error, and also understand the logic behind the answer.

Transparency#

As you can see the process that leads to an answer with the chain of thought prompting, you can verify if it's correct step-by-step. This gives more credibility to the AI systems and makes them more trustworthy.

Multi-Step Reasoning#

The chain of thought prompting enables AI systems to solve problems that require multi-step reasoning. This cognitive skill is fundamental to making decisions and understanding cause-and-effect relationships.

Chain of Thought Prompting Limitations#

Model-Dependency#

The chain of thought prompting depends on the language model that is used, therefore its capabilities are limited by it.

Prompt Generation#

It's necessary to create effective chain of thought prompts and this can take time and a lot of work. It's essential to make sure the prompts are guiding the models correctly. You will need to update the prompts regularly.

High Computational Power#

The implementation of chain of thought prompting techniques requires more computational power than the standard single-step prompting. This happens because the computer needs to generate and process multiple reasoning steps.

Discover More About Artificial Intelligence#

As you can see chain of thought prompting is an important subject that influences several tools we use daily, such as chatbots and virtual assistants, which are AI-powered. Artificial intelligence has become part of our daily life, therefore it's important to know how it works and keep yourself updated with the latest trends.