Chain Of Thought Prompting Explained: Key Insights, Benefits And Examples

Humans are naturally capable of solving problems. From the ancient era to modern times, we have successfully used our creativity, logical reasoning, and intelligence to generate solutions for conflicts. However, with the advent and evolution of AI, we are facing new challenges like the authenticity of the decisions taken by generative AI and its reasoning process.

A large language model, or LLM, is a part of machine learning. They are trained with large amounts of text datasets. These texts answer complex problems requested by a human in the user’s language. LLMs can learn patterns as well as connections between words and phrases and are fit for problem-solving step by step. Thus, the chain of thought prompting elicits reasoning in large language models (LLMs). 

What Is Chain Of Thought Prompting?

Chain-of-thought prompting is a technique in Artificial Intelligence that improves structured reasoning capabilities in large language models. The chain of thought prompting technique breaks down complex tasks into a manageable thought process. This is done by providing examples demonstrating the step-by-step reasoning process making the problem manageable. The technique improves accuracy and guides the model to handle multi-step reasoning tasks such as mathematical equations and logical reasoning chains.

Chain-Of-Thought Prompting VS. Prompt Chaining

Chain-of-thought prompting is a prompt engineering technique with some basic differences from prompt chaining. The differences are:

The Type Of COT Prompting Technique We Should Be Aware Of.

Five types of chain of thought prompt engineering techniques improve the performance of language models. They are:

Zero-Shot Prompting

When there are almost no examples, Zero-shot prompting comes in handy. Users can provide standard prompting, such as thinking step-by-step to provide the final answer to a query. Therefore, zero-shot prompting works without any examples.

One-Shot Prompting

One-shot prompting is when the amount of examples given is one. This example is similar to the targeted task.

Few-Shot Prompting

This includes providing several reasoning steps before asking the model to solve a problem. Moreover, few-shot chain-of-thought prompting helps AI models give a final output with examples of similar tasks without extensive instructions.

Automatic Chain-of-Thought (Auto-CoT)

Auto-CoT produces series of intermediate reasoning steps by gathering similar questions from zero-shot prompts. It simplifies the process as well as effectiveness of CoT prompting for various applications. 

Multimodal Chain-of-Thought Prompting

This chain of thought prompting uses text as well as images to make complex reasoning tasks easier. This prompting also makes explaining informative tasks easier by using both visual and textual data.

Read this interesting blog on how Reinforcement Learning From Human Feedback helps COT train language models to perform tasks more aligned with human goals, wants, and needs.

What Is The Difference Between Few-Shot Prompting And Chain-Of-Thought?

Chain-of-Thought (CoT) and Few-shot prompting differ in their application and approach.

What Are The Domains Where CoT Can Be Applied?

1. Arithmetic Reasoning
2. Common Sense Reasoning
3. Symbolic Reasoning
4. Question Answering
5. Natural Language Reasoning

How Does Chain-Of-Thought Prompting Work?

The process starts with writing prompts that help the AI model go through the problem step-by-step so that the intermediate steps can be generated without jumping straight to the final answer. The different strategies on how to do chain of thought prompting are:

Explicit instructions

Explicit instructions are the least-to-most prompting that breaks down a complex problem into simpler subproblems and then solves them sequentially.

Implicit instructions

Sometimes, rather than breaking down a problem, prompting may be given in a sentence or phrase, such as everything you need to know or let us work out step-by-step. This chain-of-thought prompting enables the model to think aloud as well as go through every step.

Demonstrative examples

Providing a series of examples combined with CoT prompting helps AI models understand the reasoning paths. Therefore, depending on the number of examples provided, demonstrative examples are known as one-shot and few-shot prompting.

What Is Prompt Engineering?

Prompt engineering involves creating well-structured and perfectly written prompts that are within the potential of AI models to explain and interpret. When you use LLMs, the prompts initiate the outcome. Moreover, the prompts contain instruction, context, input data, and output indicators, allowing users to understand how the model arrived at a particular response.

When Should We Use Prompt Engineering?

1. Complex tasks that require detailed reports or technical explanations.
2. When results are needed with lists, tables, essays, etc.
3. Required for generating responses on specialized topics like health, finance, engineering, etc.
4. Needed to improve the accuracy of chatbots as well as virtual assistants for user feedback.
5. To address and solve biases in sensitive sectors like legal as well as healthcare.

Using COT Prompting For Customer Service.

CoT prompting can enahnces customer support by enabling AI models to generate relevant responses. It breaks down a complex problem into sub-parts, reducing difficulty with logical reasoning. This organized approach improves response accuracy. CoT prompting ensures issues are addressed entirely and efficiently, improving customer satisfaction.

How To Maximize The Potential of LLMs With CoT Prompting?

To increase the capabilities of large language models with CoT, the following steps can be used:

1. Breaking down challenging problems.
2. Using crisp and concise language.
3. Providing contextual information.
4. Including examples.
5. Refining and improving prompts.

Therefore, chain of thought prompting can be used as a toolkit for data annotation services.

What Are The Benefits Of Chain-Of-Thought Prompting?

There are four main benefits of chain-of-thought prompting:

1. Primarily, sectors like healthcare, legal, and finance depend on problem-solving abilities. LLMs use a chain of thoughts to understand challenges better and create responses after considering probabilities and worst-case scenarios.
2. Secondly, models don’t jump to conclusions. They think through steps logically to reach results.
3. Thirdly, models are flexible and can handle new tasks without extra training because they follow logic.
4. Finally, models are good at answering questions with multiple parts clearly and in order.

Recommended reading: Small Language Models

Limitations Of Chain Of Thought Prompting

Chain-of-thought (CoT) prompting helps AI think through complex problems step-by-step, but it isn’t perfect for every task. Here are some common issues:

1. Overcomplicating Simple Tasks: CoT is excellent for complex tasks but can make easy ones more complicated than needed, causing errors.
2. Higher Processing Power: CoT uses a lot of computing power. Therefore, if smaller models use it, they may slow down or make mistakes.
3. Prompt Clarity: CoT needs clear as well as well-structured prompts. If prompts are unclear, CoT may wander and give incorrect answers.
4. Handling Large Tasks: CoT can slow down when handling a lot of information or big tasks, making it hard to use for things that need fast answers.

To conclude, as technology evolves, it’ll be interesting to see how chain-of-thought prompting evolves and becomes more straightforward yet ideal.