This research paper introduces Chain of Draft (CoD), a novel prompting strategy for Large Language Models (LLMs) designed to mimic efficient human reasoning by generating concise intermediate thoughts. Unlike the verbose Chain-of-Thought (CoT) prompting, CoD encourages LLMs to produce minimal yet informative outputs at each step, leading to comparable or superior accuracy with significantly reduced token usage and latency across various reasoning tasks. The authors provide empirical evidence using models like GPT-4o and Claude 3.5 Sonnet on benchmarks including arithmetic, common sense, and symbolic reasoning, demonstrating the efficiency and potential of CoD, while also noting limitations in zero-shot settings and smaller models. The work suggests that CoD offers a more practical approach for real-world LLM applications where cost and speed are critical.