Leveraging large language models (LLMs) has seen a significant advancement with Chain-of-Thought (CoT) prompting. CoT involves guiding an LLM through logical steps to reach a solution, which is beneficial for complex reasoning tasks. Traditionally, this process required manual crafting of effective demonstrations, which was time-consuming and prone to errors.
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