How to Train Your AI Bot with CoT Prompting

What Is Chain-of-Thought Prompting?

CoT is a prompt engineering technique that helps a large language model think through an answer step by step. It’s a way of seeing more of the brain mechanics behind the scenes. This process also improves the probability that the output will be factual.

This method not only improves the model’s performance on complex, logic-driven tasks but also reveals the reasoning behind its conclusions.

Even if the explanations do not fully align with the underlying token generation mechanics, Curran says they provide valuable insight into the model’s decision-making process.

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How Does CoT Work?

CoT prompting works by asking the model to produce intermediate steps instead of jumping to a final answer, explains Jennifer Marsman, a principal engineer in generative AI at Microsoft.

“This approach breaks down complex problems and allows the model to reason one step at a time,” she says.

The focused attention on one step of the problem and the repetition of key pieces of data help to reduce errors because there are fewer gaps in logic.

Unlike traditional one-shot prompting methods — where a model is tasked with solving a query in a single step — chain-of-thought prompting encourages the model to “show its work” by reasoning through the problem step by step. The model essentially displays individual thought components, which together gradually add up to the right answer. 

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What Are Chain-of-Thought Prompting Techniques?

Users can guide a model to use CoT by providing “few-shot” examples that showcase the behavior.

For example, in a chatbot scenario that accepts questions and gives answers, the user could provide a series of question-and-answer pairs along with the final question.

“In the provided answers, you don’t just answer the question but talk through how you arrived at that conclusion in a reasoning chain,” Marsman says. “Alternatively, you can also use zero-shot CoT prompting.”

However, there isn’t a single chain-of-thought prompting technique that’s universally applied across entire applications or tech stacks. Instead, most implementations adapt and customize these techniques to fit specific needs.

“Many setups incorporate a combination of methods,” says Curran. “Typically, this involves a single-shot learning step paired with chain-of-thought or explainability-focused elements in the prompting itself.”

These adjustments ensure the approach fits the context while encouraging reasoning and clarity in the model’s responses.

This allows developers to tailor prompting techniques, depending on the logical complexity and requirements of the task at hand.

What Is Zero-Shot Chain-of-Thought Prompting?

In zero-shot CoT prompting, a user can give the model specific instructions, such as asking it to think through a problem step-by-step.

“This explicit instruction in your prompt can guide the model to follow a similar reasoning process to arrive at an accurate answer,” Marsman says,

What Is Automatic Chain-of-Thought Prompting?

Automatic CoT prompting is a technique that generates a set of question-and-answer pairs. Often, the answers include the reasoning chain of thought and serve as few-shot examples in the prompt.

“This technique allows the model to automatically generate CoT guidance and reduces the need for humans to manually craft the few-shot examples,” Marsman says.

While the model-generated reasoning chains can sometimes contain errors, increasing the diversity of the few-shot examples can offset this problem.

What Is Chain-of-Thought vs. Few-Shot Prompting?

CoT and few-shot prompting are not opposites; in fact, they can be used together. 

In few-shot prompting, a user provides examples in the prompt to guide the model’s responses.

“These don’t have to be examples that are visible to the end user, but they can be included in the prompt to guide the model to respond in a similar style,” Marsman says.

CoT prompting, conversely, guides the model to think step by step, she adds. This can be achieved either by explicitly instructing it to do so (zero-shot) or by including examples of responses where each step is discussed and worked through (few-shot).

Both techniques tell the model to generate a response following a set thought process.

Chain-of-Thought vs. Standard Prompting

A major benefit of chain-of-thought prompting compared with standard prompting is that the model can generate better responses to problems that require processing multiple steps.

CoT also increases the chances that the final answer will be correct because it instructs a model to break down the problem into smaller, logical steps.

“If I include some few-shot examples that demonstrate chain-of-thought thinking, I am showing the model how to break the problem down and address the various pieces of it one at a time,” Marsman says.

And when an LLM tackles a big question in digestible bites, data is less likely to become jumbled, and hallucinations are less likely to occur.

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What Are Some Applications of Chain-of-Thought Prompting?

CoT prompting is particularly helpful with reasoning tasks such as math word problems, puzzles, coding challenges and complex logical questions. 

“CoT can be applied in tasks that require step-by-step reasoning, such as analyzing compliance documents, generating summaries or synthesizing information from multiple sources,” she says.

In an enterprise context, CoT prompting is often combined with other methods such as single-shot or few-shot learning or plan-and-solve reasoning, or approaches involving external data sources.

“These blended strategies enhance model performance in diverse applications, such as assembling data from multiple systems, summarizing documents or generating detailed responses for end users,” Rowan adds.

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