Chain-of-thought prompting is a technique used in the field of Natural Language Processing, particularly with Large Language Models (LLMs), to enhance the model's reasoning capabilities by guiding it through a series of intermediate steps or thoughts. This method involves structuring prompts in a way that encourages the model to break down complex problems into smaller, manageable parts, thereby improving its ability to generate coherent and contextually relevant responses. By simulating a logical progression of ideas, chain-of-thought prompting helps LLMs tackle tasks that require multi-step reasoning, such as mathematical problem-solving, logical deduction, and complex decision-making processes. This approach leverages the model's capacity to follow a narrative or sequence, ultimately leading to more accurate and insightful outputs.
The LLM Knowledge Base is a collection of bite-sized explanations for commonly used terms and abbreviations related to Large Language Models and Generative AI.
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