Trend Alert: Chain of Thought Prompting Transforming the World of LLMs

According to the latest report by McKinsey, generative Artificial Intelligence (despite being nascent) is currently being used by more than 60% of businesses to improve various aspects of their businesses. Among all those businesses harnessing the power of generative AI, the top AI performers are using this AI tool to bring down expenses, add value to their business by garnering insights from AI, and come up with new revenue sources for the company. 

One of the main reasons so many businesses can unleash the power of generative AI is; that this type of AI model can perform complex reasoning. But when it comes to harnessing the real power of generative AI at its full potential, the answer lies in the way businesses use prompting. This is where the Chain of Thought prompting comes into the picture. 

Doesn’t matter in which way you are using or working on generative Artificial Intelligence, delving into the world of Chain of Thought Prompting is compulsory and this is what we are going to do in this blog post. So, tighten your seatbelt, and let’s take a stroll into the world of Chain of Thought Prompting with us through this blog post. 

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Chain of Thought Prompting: Sneak Peak

Have you ever thought of teaching your language model to begin thinking in a step-by-step way? just like you would wish a human to do? Well, if this thought has ever clicked in your mind then this is what  Chain of Thought Prompting (CoT) is all about. CoT is just like assigning a well-trained tutor to your large language model so that the tutor can pilot the model through complicated problems. 

By using the power of CoT, you don’t just toss a complicated query to your Large Language Model (LLM), and then expect it to do wonders instead you break that query into small pieces. After breaking the query into bite-sized pieces, you teach the LLM to solve every piece one by one until it reaches the end of the query. 

So, CoT is a method of guiding your LLM to begin logical reasoning by offering different examples of step-by-step solutions. 

Birth, Basics, and Use Cases of Chain of Thought Prompting

Genesis 

Chain of Thought Prompting was introduced to the world because there was a need to enhance the interpretability of AI decision-making methods. Before the introduction of CoT, the traditional models provided users with only the answers for which there was no explanation. This type of model kept the user in the dark regarding the real reason behind the answer. 

But with the advent of CoT, users can know the reason behind every intermediate step involved in the process that led to the final answer. This adds logic to the entire processing of the question asked to the LLM, just like a human and this is how CoT was introduced to the world. 

Basics

Chain of Thought Prompting is a type of prompt engineering technique that has been designed with the aim of improving the performance of different types of language models by simply guiding them on problems that need decision-making, calculations, and reasoning by structuring the input prompt in a way similar to human decision-making and reasoning. 

With the help of Chain of Thought Prompting, large complicated tasks can be broken down into bite-sized pieces and this helps the language model to further process the data into logical sequence. So, CoT asks the LLM to not only give the final result but also the information about the series of intermediate steps that have resulted in the final answer. 

When a business or top AI performer uses CoT in the right way, he can achieve better precision and the right result as well. 

Using the CoT model for LLMs to show intermediate steps has given positive results. The “Chain-of-Thought Prompting Elicits Reasoning in Large Language Models,” paper published by the Google Brain research team in 2022 at the NeurIPS conference showed that CoT has surpassed the general prompting method on paramount parameters like common-sense, arithmetic, and symbolic reasoning. 

Use Cases

To better understand the concept of CoT, we will need to understand how it is being used in real life. Here are some common implications of CoT that are currently changing the way LLMs are used. 

Writing Assistance and Content Creation

Content is the most cardinal part of the online world. And with experts claiming that more than 90% of online content will be AI generated by 2026, businesses need to step up and bring AI into action. Luckily,  CoT has its own method of contributing to the realm of AI. 

When it comes to content creation and writing assistance, updated AI models like GPT-3 paired with CoT can become an invaluable asset to any firm or person. The mix of both can equip anyone with consistency in the writing style, create coherent content that matches the intent of the user, and even fathom the narrative context. 

Dynamic Conversational Agents

More than 80% of the marketing and sales leaders are currently using the power of chatbots to improve customer experience and CoT can help these leaders to further level up the use of chatbots. 

The CoT model can be used to enhance customer experience in both conversational agents and chatbots. When such models are used, they can engage customers in a dynamic conversation that looks natural. With the use of CoT, chatbots can fathom the flow of discourse and then answer in a more logical and contextually grounded way. 

Problem-Solving and Code Generation 

In 2022, the AI coding market was valued at USD 4.1 billion and according to the experts, this market is expected to achieve a CAGR of 225 between 2023 and 2032. This means that developers need to harness the power of AI models like CoT instead of considering them as their replacement. 

CoT can help developers with problem solving. By taking into account the entire history of coding and fathoming the evolving trends and context, a CoT model can come up with code snippets that match the developer’s aim and adhere to build coding patterns. 

 Educational Applications 

According to the survey conducted by Forbes Advisor in 2023, the use of AI in schools has positively affected the entire teaching process and the learning process. This means that the future of generative AI models like CoT in the education sector is glimmering with opportunities. 

The use of CoT in the education sector can lead to a better tutoring system. Because CoT comes equipped with the ability to retain context, it can easily understand the journey of every student on an individual level. In addition to this, CoT can also offer personalized assistance to every student and give tailored responses to individual progress. 

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How does Chain of Thought Prompting Work?

In layman’s language, CoT simply works by breaking down a question or a problem into bite-sized, sequential steps. Every step broken down by CoT offers particular guidelines to the large language model and this is what helps the model to narrow down its focus on only relevant data. The CoT model can be in the form of code, text, and even images. 

After the prompts of CoT are provided to the LLM, the language model is then ordered to solve the query using the data it has been provided with. In such a case, the LLM has to methods to solve the query:

Simply follow the steps that have been provided in the Chain of Thought Prompts Or, the LLM has the option to come up with its own unique steps

The most important thing to note here is that there is no need to make adjustments to any model weight while using the CoT. This clearly shows that there is no need to worry about the architecture or size of LLM while using CoT. 

How can CoT be implemented in the workflow?

If you are planning to use CoT for your LLM to get more detailed, logical, and consistent results, you need to follow the below-mentioned steps:

• Locate the main task 
• Break the bigger task into bite-sized pieces
• Build prompts for every small piece or what is more commonly known as subtask 
• Make sure that every prompt follows the previous one in a logical manner

Why Sequential Logic and Prompt Engineering are necessary?

When it comes to the success of CoT, both prompt engineering and sequential logic becomes necessary. 

Every prompt used for the subtask should be built on the data provided in the previous one. Such a method makes sure that the model includes all the necessary aspects before making a decision and this is what makes sequential logic key to better use of CoT. 

On the other hand, prompt engineering is also crucial to CoT. By taking precautions and following the right method while crafting every prompt, a user can guide the reasoning process of a model without any hassle. Such a method involves choosing the right language and making sure that there is proper clarity in every prompt used for the subtasks. 

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Benefits of Chain of Thought Prompting 

Model Debugging 

Chain of Thought Prompting can help the user with model debugging as well. It can also improve the structure by making the process through which any type of large language model can finally give an output that is more transparent. Since Chain of Thought Prompting to follow a reasoning process, it can give the user and developers better insight into how the model reached a specific answer. 

  Improved Accuracy 

One of the most important advantages of using CoT in LLM is better accuracy. Since all the required information is considered by the LLM because of the logical sequence of prompts, the responses by the LLM are more precise and contextually appropriate. 

 Enhanced Problem Solving

For every type of question that needs challenging problem-solving, the Chain of Thought Prompting is considered to be highly effective. Since CoT works on the basic principle of breaking down large problems into smaller steps, this leads to insightful solutions to even the most complicated problems. 

Improved Coherence 

Even the coherence of the model’s output can be enhanced by the use of Chain of Thought Prompting. CoT offers a clear path or we can say guides the model through the process in the simplest way. This minimizes inconsistencies and makes sure that all the responses provided by the LLM are logically structured. 

Enhanced Control 

Another most talked-about advantage of CoT is improved control. CoT offers the user a more structured method to interact with LLMs. Such interaction offers better control over the output and this also minimizes the risk of unintended output. 

Future of Chain of Thought Prompting 

In the future, Cot is expected to witness further advancement as soon as the model architecture evolves. The current research and development efforts are aimed at improving contextual understanding, addressing current challenges, and enhancing response coherence. Also, the future models of CoT will come up with user-focused customizations. This will open new opportunities for defining the nature and scope of the CoT. 

In addition to this, the trend of building transparent and more explainable AI models will also have a huge impact on the future of Chain of Thought Prompting. Such types of AI models will give users better insights into how the large language model can retain context, interpret prompts, and generate better responses. 

Chain of Thought offers a significant vault in the features of large language models, introducing the users to a new world of LLM where not only the output but the entire process and the reason behind every subtask and answer will be transparent, coherent, and consistent. From improving content generation to enhancing customer experience with the help of chatbots and aiding in problem-solving, CoT is going to reshape the realm of AI-driven interactions. So get ready to make CoT a pivotal part of your AI architecture so that your business can stay ahead of the competition.