Chain-of-thought Prompting

Don’t Panic Consulting Definition

Chain-of-thought prompting is a technique in artificial intelligence where a model is guided to generate responses by breaking down complex problems into a series of simpler, logical steps, similar to how a person might think through a problem step-by-step.

MIT/Sloan Definition

Chain-of-thought prompting is when you use a series of intermediate reasoning steps to improve the accuracy and applicability of answers generated by LLMs (Bubeck et al., 2022).

AI-Generated Perspectives

Different AI tools provide varying perspectives on this concept:

Apple Intelligence

Chain-of-thought prompting is a technique for generating text that resembles a human-generated conversation. It works by generating a chain of text, where each word is generated based on the previous word. The chain of text is then presented to the user as a prompt, and the user is asked to write a response. The response is then used to generate the next word in the chain, and so on.

Gemini Advanced

Chain-of-thought prompting is a technique used to improve the reasoning abilities of large language models (LLMs). It encourages the model to break down complex problems into a series of smaller, more manageable steps, similar to how humans think through problems.

Think of it like this:

Instead of asking an LLM a question directly and expecting a single answer, you guide it through the reasoning process by providing a few examples of how to solve similar problems step-by-step. This “chain of thought” helps the model understand the underlying logic and apply it to new situations.

Here’s how it works:

Provide a few examples: Show the LLM a few questions with their corresponding answers, but also include the intermediate reasoning steps that lead to those answers.

Ask a new question: Present the LLM with a new, unseen question.

Encourage reasoning: Prompt the LLM to “think step by step” or “let’s think about this” to encourage it to generate its own chain of thought before arriving at the answer.

Example:

Question: Roger has 5 tennis balls. He buys 2 more cans of tennis balls. Each can has 3 tennis balls. How many tennis balls does he have now?

Chain-of-thought prompt:

Roger starts with 5 balls.

2 cans * 3 balls/can = 6 balls.

Roger has 5 + 6 = 11 balls.

Answer: 11

By seeing this example, the LLM learns to break down the problem into smaller steps, perform the necessary calculations, and arrive at the correct answer.

Why is chain-of-thought prompting effective?

Improved reasoning: It encourages LLMs to explicitly demonstrate their reasoning process, leading to more accurate and logical answers.

Better generalization: It helps LLMs learn to apply reasoning skills to new problems, even if they haven’t seen those specific problems before.

Increased transparency: It provides insights into how the LLM arrived at its answer, making it easier to understand and trust its decisions.

Types of chain-of-thought prompting:

Manual chain-of-thought prompting: Manually creating examples with reasoning steps.

Automatic chain-of-thought prompting: Using LLMs themselves to generate reasoning chains.

Zero-shot chain-of-thought prompting: Using prompts like “Let’s think step by step” without providing any examples.

Applications of chain-of-thought prompting:

Solving math word problems

Answering common sense questions

Generating explanations for complex concepts

Performing multi-step reasoning tasks

Chain-of-thought prompting is a powerful technique that enhances the reasoning abilities of LLMs, making them more capable of solving complex problems and providing more insightful answers.

OpenAI GPT

Chain-of-thought prompting is a technique used in natural language processing where a model is guided to generate a sequence of intermediate reasoning steps that lead to the final answer. This approach helps improve the model’s performance on complex tasks by breaking down the problem into smaller, more manageable parts, allowing the model to better understand and solve the task.

Claude (Anthropic)

Chain-of-thought prompting is a technique that encourages large language models to break down complex problems into smaller, logical steps before reaching a final answer. By explicitly showing the reasoning process, the model is guided to “think” through problems step-by-step, similar to how humans solve problems. This approach typically involves including examples that demonstrate the intermediate reasoning steps, leading to more accurate results, especially for tasks requiring mathematical reasoning, logical deduction, or multi-step problem solving. The technique has been shown to significantly improve performance on complex tasks compared to standard prompting methods where only the final answer is provided.

Business Context

Understanding Chain-of-thought Prompting is crucial for modern businesses navigating digital transformation. As AI continues to reshape industries, this concept becomes increasingly important for strategic planning and implementation.

Learn More

For a more detailed exploration of Chain-of-thought Prompting, Detailed Explanation.

Remember: Technology should empower, not overwhelm. At Don’t Panic Consulting, we help you understand and implement these concepts in ways that make sense for your business.