A company is using a language model to solve complex customer service inquiries.

The prompt explicitly instructs the Large Language Model (LLM) to perform a step-by-step reasoning process before arriving at the final answer. The instructions lay out a sequential series of intermediate steps: "first identify," "then check," "if a solution exists, explain," "if not, escalate."

This technique is known as Chain-of-Thought (CoT) Prompting. CoT is a powerful prompt engineering technique where the user or developer explicitly includes intermediate reasoning steps in the prompt. This guides the model to break down a complex, multi-step problem into smaller, manageable, logical steps, significantly improving its reasoning ability and the accuracy of its final output for complex queries like customer service troubleshooting or multi-step analysis.

Zero-shot (A) would be the raw question without any structure.

Few-shot (B) would involve providing examples of successfully solved problems.

Role-based (C) would involve assigning a persona (e.g., "Act as a customer service expert") but would not explicitly mandate the sequential process.

The inclusion of the explicit steps ("first identify," "then check," etc.) is the defining characteristic of Chain-of-Thought prompting.

(Reference: Google's courses on Prompt Engineering classify Chain-of-Thought prompting as the technique that improves reasoning by explicitly giving the model a series of sequential, intermediate steps to follow to arrive at a better answer for complex tasks.)

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