<ul><li>Chain-of-Thought prompting is a technique used to guide Large Language Models (LLMs) by breaking problems into smaller logical steps to improve accuracy and logical reasoning.</li><li>This technique involves providing step-by-step instructions to the LLMs, encouraging them to think through a problem sequentially like a human.</li><li>Advantages of Chain-of-Thought Prompting include improved accuracy, scalable problem-solving, enhanced debugging, and better generalization to unseen problems.</li><li>Best practices for Chain-of-Thought Prompting include using analogous examples, being explicit in outlining steps, iterating and refining prompts, and leveraging few-shot learning for complex tasks.</li></ul>

Chain-of-Thought Prompting Technique in LLMs

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