Chain-of-Thought Prompting

Chain-of-Thought (CoT) questions have become a staple in AI/ML engineer interviews at top tech companies. These companies often probe candidates on CoT because it’s a cutting-edge concept in modern AI—essentially teaching AI models to “think out loud.” CoT prompting was introduced by Google researchers in 2022 as a way to get large language models to reason through problems step-by-step. In interviews, you might be asked about CoT to see if you understand how today’s most advanced AI models tackle complex tasks by breaking them down into intermediate reasoning steps. This topic is important because it underlies how models like GPT-4.1 handle reasoning, a core capability for math word problems, logic puzzles, and multi-hop questions that simpler techniques struggle with.

Major AI employers emphasize CoT in interviews since it represents a research breakthrough and a practical skill. CoT prompting has led to state-of-the-art results on challenging benchmarks, enabling models to solve problems that were previously out of reach with standard prompting​. Knowing about CoT signals to interviewers that you stay up-to-date with AI advancements and can apply modern techniques to real-world problems. It also shows that you grasp how AI models “think,” which is crucial for roles involving prompt engineering, model development, or AI problem-solving. In short, CoT questions are important because they test your understanding of how to coax better reasoning out of AI—a skill highly valued at companies pushing the frontier of ML.

In this breakdown, we’ll look at the key aspects an interviewer expects:

• Why chain-of-thought prompting became necessary to solve problems like math word problems, logic puzzles, and multi-hop reasoning, where standard prompting falls short;

• How CoT changes model behavior, making intermediate reasoning steps explicit and improving accuracy and interpretability on complex tasks;

• How to practically apply CoT prompting to guide a model through a multi-step task, using strategies like zero-shot CoT (“Let’s think step-by-step”) and few-shot CoT (example-driven prompting);

By the end, you’ll be prepared not just to define Chain-of-Thought prompting, but to explain how it transforms a model’s reasoning flow—and why mastering this technique is essential for building more reliable, interpretable AI systems today.

What is CoT?

Imagine you’re teaching a student to solve a tricky math problem. They might rush and guess incorrectly if you ask them for the ...