Welcome to the fourth blog in WeCloudData’s Prompt Engineering Series! In the previous blog we explored basic prompt engineering techniques, such as zero-shot prompting and few-shot prompting. These techniques are effective in helping large language models to produce contextually relevant output. This blog is an introduction to a more advanced technique known as chain of thought prompting or CoT. CoT allows LLM’s to break down complex tasks into small manageable parts just like how humans solve the problems.
Let’s dive into what chain-of-thought prompting really means, how it works, and how you can use it to enhance the performance of AI in real-world applications.
What is Chain of Thought Prompting?
Chain of thought prompting breaks down a complex question into smaller,manageable, and logical parts that resemble a train of thought. Introduced in Wei et al. chain of thought method helps the model solve the query in a step-by-step manner rather than directly answering the question. Using CoT AI is guided to think aloud thus enhancing its reasoning ability before reaching a conclusion. Research shows that CoT significantly improves the ability of large language models to perform complex reasoning.
For complex tasks several chain-of-though rollouts can be performed to choose the most commonly reached conclusion. For example: If the question is “Square root of 25 is ?”, the AI model might perform several rollouts leading to answers like “5,” “The square root of 25 is 5,” and “5 is the square root of 25.” Since all rollouts lead to the same conclusion, “5” would be selected as the final answer.

Types of Chain of Thought Prompts
Like other techniques of prompt engineering chain-of-thought can be adapted to different scenarios using two main types:
Zero-shot COT Prompting
Zero-shot COT instructs the model to break down its reasoning without giving any examples. Introduced by Kojima et al. it involves adding “Let’s think step by step” to the original prompt. Let’s try a simple problem and see how the model performs:
Prompt:”I went to the bakery and bought a dozen donuts. I ate 3 glazed donuts and gave 5 chocolate donuts to my friends. How many donuts do I have left?”

Prompt: ”I went to the bakery and bought a dozen donuts. I ate 3 glazed donuts and gave 5 chocolate donuts to my friends. How many donuts do I have left?
Let’s think step by step.”

It’s impressive that this simple prompt is effective at this task. This is particularly useful where you don’t have too many examples to use in the prompt.
Few-Shot Chain-of-Thought Prompting
The few-shot Chain of Thought prompting technique involves providing the AI model with a few examples of how to solve a problem by explicitly defining the reasoning steps. This guides the model to generate its own chain of thought before arriving at the final answer. Let’s try a simple example and see how the model performs by following this link.
Prompt: A customer buys a $20 item and pays with a $50 bill. How much change should the cashier give them? Solution: Start with the paid amount: $50. Subtract the cost of the item: $50 – $20 = $30. Thus the cashier should give the customer $30 in change.
Now solve this: A recipe calls for 3 cups of flour, but you only have 1 cup. How much more flour do you need?

Advantages of Chain-of-Thought (COT) Prompt
- Improved Reasoning: By mimicking human problem solving skill, chain of thought prompting ensures better accuracy for complex tasks.
- Transparency: The step-by-step reasoning provides clarity and builds trust in AI’s decision-making process.
- Enhanced LLMs Learning: CoT enhances the learning process of large language models. Example Prompt: ”If all cats are mammals and all mammals drink water, do cats drink water? Explain step by step.”
- Mathematics and Arithmetic: chain of thought prompting helps solve multi-step word problems by guiding calculations through each necessary step. Example Prompt :”What is the square root of 64? Explain step by step.”
Applications of Chain-of-Thought (COT) Prompting
Education : Ideal for tutoring students, it makes problem-solving easier by providing step-by-step solutions and explanations.
Programming: The chain of thought method simplifies difficult coding problems and helps programmers in debugging efficiently.
Healthcare: Assists in diagnosing conditions by reasoning through symptoms systematically.
Research: CoT Produces well-structured and logical responses to support scientific inquiries.
What’s Next?
At WeCloudData, we believe in the transformative power of AI. Our courses are designed to empower you with hands-on skills to navigate the world of LLMs and build AI-driven solutions that deliver real impact.
Next in WeCloudData’s Prompt Engineering Series, we’ll explore Role-Play Prompting. Stay tuned as we continue to discover how to get the most out of AI and LLMs!