Chain of Thought Prompting: A Definitive Guide

Large language models such as ChatGPT- 4, or Claude 3.5 Sonet are excellent at generating relevant responses, considering the vast amount of training data and the kind of prompts users ask. Despite being powerful, these models find it challenging to resolve complex or multi-step tasks. That’s when prompt engineering comes into play. Out of the various prompt engineering techniques, chain-of-thought prompting is the best. 

Do you know why? Because it leverages NLP to break large problems into smaller, logical steps to derive thoughtful and accurate responses.

For AI developers, business owners, and enthusiasts who are looking forward to leveraging Generative AI services, becoming familiar with the chain of thought is essential. This technique has the power to improve the performance of AI systems, particularly for applications that demand precision and detailed problem-solving.  

Here, we will explain what is a chain of thought prompting, its types, use cases, applications, benefits, and limitations in detail. 

So, let’s get started. 

What is a Chain of Thought Prompting?

Chain of Thought prompting is a prompt engineering method that takes the reasoning abilities of LLMs to a new level. How?  By urging the models to break down their reasoning into multi-step thought processes.

Instead of expecting a direct response to the queries, the user wants the model to think step-by-step and respond accordingly. To achieve this, the model is first presented with several examples that follow the exact same process. This not only enhances the model’s ability to solve complex problems but also helps it build logical connections and offer more accurate and contextually relevant answers. . 

How Chain of Thought Prompting Work?

Chain of thought prompting works on the core principle of breaking down the complex problem into smaller and more logical parts. This functions similarly to how a human thinks about an issue. We, as humans, consider different scenarios and aspects of any problem before arriving at the final answer. 

By prompting the LLM to consider a pre-defined path, the LLM urges the chain of thought to follow an intended reasoning process. Hence, the model is now able to deliver accurate, in-depth processes, especially for multi-layered queries. 

Let’s understand this by considering the simple chain of thought prompting examples.

To understand the difference between standard and chain of thought prompting, we will consider a simple math problem.

In standard prompting, the AI directly answers the question by considering its keyword patterns. This results in a false response or an oversimplification of a simple question. 

On the other hand, the chain-of-thought prompt method structures the question into a simple step-by-step prompt. Then, it processes every component in a given order, resulting in a more accurate and reliable response.

Bonus Read: LLM vs Generative AI

Types of Chain of Thought Prompting

Chain of thought prompting doesn’t follow a one-size-fits-all approach. Even since its introduction, it has evolved into a range of variants, each optimized to address a particular set of questions and improve the model’s reasoning capabilities like never before. 

AI developers, NLP practitioners, and businesses that want to leverage CoT fully should know about these techniques. To simplify your task, we will examine the leading chain of thought prompting variants and examples for better clarification.

1. Zero-Shot Chain of Thought Prompting

Zero chain-of-thought prompting leverages the pre-defined prompts or inherent knowledge to answer any of the prompts without the need for specific examples or optimizing any of the models. 

Generally, Zero-Short CoT forces you to add “Let’s think step by step” in the original prompt to direct the LLM to follow a reasoning process. This approach is excellent when you are dealing with complex problems that don’t have enough training examples in the prompt. 

The model extracts the reasoning and answers the query using two prompts. Let’s look at that.

Reasoning Extraction:  The LLM considers the question and forms a chain of reasoning to reach the desired answer. To achieve the required output, we just have to give LLM a prompt that includes the question followed by the phrase “Let’s think step-by-step.” 

Answer Extraction: In this step, we extract the answer from the LLM’s response. Further, we connect the prompt, the generated sentence, followed by the triggered phrase “The answer is”. This enforces the LLM to provide the answer.

Let’s understand this with a simple example given below.

2. Automatic Chain of Thought Prompting

The main purpose of this CoT prompting technique is to reduce the manual effort required in crafting prompts by automatically generating and choosing a step-by-step reasoning path. In other words, the model detects patterns from existing examples and leverages them on new problems.

Here, instead of relying on any external guidance comprising multiple steps, it refers to itself for discovering prompts and adapting them to new situations. The model is best for handling tasks requiring spontaneous but structured problem-solving.

Automatic chain of thought prompt consists of the two main stages as follows:

Question Clustering: Initially, they divide the question of the given dataset into a few clusters.

Demonstration Sampling: When they possess these question groups, they choose a question from every cluster and then generate the reasoning chain leveraging the Zero-Shot CoT. 

Have a look at the below example for better understanding.

3. Multimodal Chain of Thought Prompting

As the name suggests, Multimodal chain of thought prompt leverages the power of Multimodal AI to integrate input from a wide range of modalities, such as text, images, and audio, to process and integrate a wide range of information for complex reasoning tasks. 

Multimodal prompting integrates multiple modalities into a two-stage framework. The first step is known as rationale generation, where the model is incorporated with text and images and requested to provide a rationale explaining how these things are related to each other. The second stage in the framework is referred to as answer rationale, where the model utilizes the output generated from the first stage to come up with an accurate answer. 

Let’s understand this with a simple image given below.

From this example, we can say that multimodal AI considers visual data along with text data and applies progressive reasoning across diverse data types to arrive at an accurate answer.  

Applications of CoT Prompting

From solving simple math problems to handling complex tasks, the chain of thought prompting leverages the potential of AI to tackle challenges one by one. Let’s look at the applications of CoT across different fields.

1. Arithmetic Reasoning

Solving real-world math problems poses a considerable challenge for large language models. By leveraging the chain of thought prompting with a 540B parameter language model, the model offers next-level performance on two popular benchmarks: MultiArith and GSM8K. 

Here,  CoT prompting enables the model to tackle multi-step math problems efficiently by breaking complex problems into smaller manageable steps. Also, CoT maintains the calculations easily by not skipping or making assumptions.

In general, the large language models are highly benefited through this approach, thus enhancing the accuracy and efficiency of arithmetic reasoning. Ultimately, the application represents the CoT’s ability to resolve complex problems with utmost accuracy.

2. Commonsense Reasoning

CoT prompting can do wonders in commonsense reasoning. Here, large language models break down hypothetical or situational scenarios, and then they understand and interpret the physical and human interactions, considering general knowledge.  

Commonsense reasoning is applicable in various tasks, such as CommonsenseQA, StrategyQA, date understanding, and sports understanding. The performance of these tasks is directly proportional to the model size, which means it improves with an increase in model size. 

Here, CoT breaks down the model’s thought process by throwing the right set of prompts, and improves accuracy, particularly in the case of understanding context and inferring conclusions such as sports understanding.

3. Symbolic Reasoning

Symbolic reasoning is a cognitive process involving various symbols and character strings representing multiple concepts, objects, and relationships. The method further involves manipulating these symbols and leveraging logical rules to reach a conclusion. 

Symbolic reasoning is difficult to achieve in LLMs that leverage standard prompting, as they can’t capture the complexities of symbolic tasks. With chain-of-thought prompting, the model can implement step-by-step logic and handle various puzzles, algebraic problems, or logic games with precision.

Here, CoT is useful to enhance performance in various tasks that involve evaluation, such as last-letter concatenation in a word and coin flip.

This application showcases the CoT’s ability to remove all the limitations of symbolic reasoning, thus ensuring that AI becomes adaptable in different fields that involve strict logical consistency. 

4. Question Answering

CoT prompting enhances the question-answering process by breaking down complex questions into smaller, digestible steps. This allows the model to interpret the question’s framework and the relationship between the components. Every component emphasizes a particular part of the question, which further allows the model to detect relevant information effortlessly.

The chain of thought prompt is also responsible for multi-hop reasoning. Here, the model constantly collects and combines information from numerous sources and documents on the web. This further enhances the inference and produces more precise and nuanced answers that cover all the angles. 

Apart from this, CoT prompts force the model to specify the reasoning steps, which helps prevent basic errors and biases in the responses. Also, CoT gives a detailed explanation of how the LLM reached a particular conclusion.

Use Cases of Chain of Thought Prompting

Here are some of the potential use cases of the chain of thought prompting.

• Gen-AI Backed Customer Service Chatbots  

Chain-of-thought prompting ensures that complex customer queries are converted into smaller, easily manageable parts. Thus, it allows the best AI chatbots in the market to offer accurate and contextual responses just like humans. By constantly assisting the chatbot in every troubleshooting step, CoT prompting promises to deliver logical, sensible, and wise decisions that customers can implement. It ultimately leads to quick issue resolution and increased customer satisfaction. 

• Research and Innovation

Researchers leverage the CoT prompting to organize, analyze, and synthesize their thoughts to identify patterns or test hypotheses, which further helps in solving complex problems. This structured approach accelerates the entire discovery process, ensuring that the tool is responsible for various scientific and technological breakthroughs.

• Healthcare Decision Support

AI in healthcare has the potential to transform the entire industry. Chain of thought is one such part of AI that improves LLMs’ ability to diagnose complex medical conditions. By running different chains of thoughts on LLM linked to the patient’s symptoms, medical histories, and diagnostic criteria, healthcare professionals can fetch accurate and relevant insights, which results in enhanced patient care and treatment outcomes.

• Legal Document Analysis and Summarization

In the legal domain, LLMs can be trained to break down complex legal documents into small steps, starting from analyzing documents, identifying risks, and logically summarizing the most essential sections. This structured approach leads to detailed analysis and the generation of concise summaries as per the legal requirements. Thus, CoT is responsible for making the legal content simple to review, understand, and make decisions for tasks like legal research, contract management, and compliance work.

• Financial Analysis and Reporting

Using chain of thought prompting in LLMs allows financial analysts to scrutinize financial reports, assess risks, and determine valuable insights and market trends step-by-step. This effective method of analysis and forecasting enables the LLM to produce accurate and data-driven financial recommendations and reports and allow users to make effective decisions related to investment strategies.

• Education and Tutoring Systems

LLMs in the education sector can utilize the chain of thought prompting to guide students to understand complex topics in well-known subjects such as math, science, and language learning in a systematic manner. It can also enable students to understand the sequence of steps required to solve a problem. Ultimately, it makes LLM-powered tutoring software or tools more relevant and effective.

Also Read: Role of AI in Education

• Supply Chain Optimization

Talking about supply chain management, the chain of thought prompting supports LLMs in analyzing and optimizing the logistics networks by breaking down every component, such as the logistics, inventory levels, transportation routes, and demand forecasts, into manageable parts. This streamlined method results in reduced operation costs, improving performance, and building a strong resilience of the supply chain in multiple industries, such as automobiles, fashion, pharmaceuticals, technology, and more.

Benefits of Chain of Thought Prompting 

Here are some essential benefits users can expect from CoT by leveraging it in large language models.

• Enhanced Reasoning Accuracy 

Although LLMs can generate coherent text, they lack logical reasoning. CoT prompting allows the large language models to break down complex reasoning tasks into smaller and easily sequential steps, which leads to increased accuracy of reasoning. As only the relevant information is considered in these steps, it results in more precise and contextually appropriate outputs.

This CoT approach is absolutely great for math problems, logical puzzles, and multi-hop reasoning, as it requires multiple steps of reasoning to reach reliable and desired output.

• Attention to Detail

The step-by-step explanation approach ensures that the model considers every element in the problem or query so that none of the essential information goes unnoticed. This approach looks similar to teaching methods that require a detailed breakdown of any problem for a more thorough and nuanced understanding. It works absolutely well in tasks that require utmost precision, such as legal analysis, scientific research, and educational content.

• Improved Interpretability

By leveraging the chain of thought prompting, you can remarkably enhance the interpretability of the LLM output. Moreover, the large language model becomes transparent by mentioning the step-by-step reasoning behind every answer. This even allows the users to become familiar with how the conclusions are derived. 

This enhanced transparency is the reason behind the model’s AI-generated responses, which enable users to comprehend the rationale behind various decisions and suggestions. Lastly, the model can do wonders in fields like healthcare, law, and finances, as understanding the rationale is non-negotiable.

• Diversity

Chain of thought prompting is highly flexible because it can be applied to broader tasks. These include arithmetic reasoning, commonsense reasoning, complex problem-solving, and more. Its versatile utility makes it suitable for adaptation to multiple domains, which makes this tool further responsible for improving the performance of LLMs across different industries.  

Limitations of Chain of Thought Prompting

Here are some of the limitations that you should know before implementing the chain of thoughts prompting in your model.

• Requires High-Computational Power

Chain of thought prompting involves breaking down tasks into multi-step reasoning, which requires high computational power and consumes more time than single-step prompting. This enhanced load probably slows down the response time and requires more robust hardware, thus making it unsuitable for applications that need high-speed processing. Lastly, more power also involves high costs, so startups and small businesses can’t adopt it because of limited resources.

• Needs More Extensive Prompt Engineering

The overall efficiency of the chain of thought prompting is directly dependent on the quality of prompts that intrust the LLM via the desired thought process. Poorly designed prompts often lead to poor reasoning paths, which ultimately results in false outputs. 

This excessive dependence on the prompts requires proper design, testing, and refinement to get the desired output from the model. For the same, one needs to have technical expertise to become familiar with the task at hand and the model’s behavior. All of these things can be time-consuming and resource-intensive. 

• Models Overfitting

Whenever the models are trained for a particular type of style or pattern of reasoning in the prompts, it increases the risk of overfitting. This indicates that the model might work as required for familiar tasks; however, it might struggle to deal with novel or unfamiliar situations. It ultimately reduces the generalization and adaptation abilities of the model for new tasks.

• Limited Contextual Understanding

Even though CoT improves reasoning abilities, it might not address the limitations involved in contextual understanding. If a particular LLM doesn’t possess knowledge or context of a specific domain, even by following a step-by-step reasoning process, the model won’t be able to deliver the right output. Thus, it requires extensive training data.

Transform Your LLM with Chain of Thought Prompting

Chain of thought prompting will completely change the way we interact with all the large language models. By instructing the models to tackle complex tasks with structured reasoning, the techniques improve problem-solving capabilities, reduce biases, and make intermediate reasoning steps more interpretable. This helps to achieve precise, coherent, and valuable output across a wide range of applications. Hence, the chain of thought prompting can be powerful for various generative AI applications. 

If you want to leverage this technique for your LLM, contact us for AI strategy consulting services. We have AI experts who possess extensive knowledge of chain of thought prompting. Hence, we can understand your requirements and integrate them into your model to achieve improved performance and the best output as per your needs.