International Journal For Multidisciplinary Research

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Chain-of-Draft Prompting: A Structured Approach to Efficient AI Reasoning

Author(s) Dr. Prasuna VG VG, Dr. Sashibhushana Rao Majji
Country India
Abstract Recent advancements in prompt engineering have significantly enhanced the reasoning capabilities of AI models. Large Language Models (LLMs) have transformed complex reasoning through Chain-of-Thought (COT) prompting. While effective, COT's verbosity leads to higher computational costs and latency, posing challenges for efficiency-driven applications. Chain of Draft (COD) [Xu et al., 2025] offers a streamlined alternative, inspired by human problem-solving patterns, where only essential information is recorded. Chain-of-Draft Prompting (COD-P) introduces a structured framework that optimizes iterative refinement in AI-generated responses. Unlike traditional methods, COD-P systematically guides models through incremental drafts, enabling them to develop more coherent, context-aware, and accurate outputs. This approach leverages hierarchical scaffolding to improve logical consistency, minimize hallucinations, and enhance problem-solving efficiency. Experimental evaluations demonstrate that COD-P outperforms conventional prompting techniques, improving coherence (+19.4%) and accuracy (+13.3%) in complex reasoning tasks. The findings underscore the importance of structured prompting in advancing AI reasoning and provide a foundation for future improvements in interactive AI methodologies.
Keywords Prompt Engineering, Iterative Refinement, Structured Prompting, Hierarchical Scaffolding, Logical Consistency, Complex Problem Solving, Cognitive Processing, Hallucination Minimization, Scalable AI Solutions
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 7, Issue 3, May-June 2025
Published On 2025-06-01
DOI https://doi.org/10.36948/ijfmr.2025.v07i03.46661
Short DOI https://doi.org/g9m2d2
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