Prompts for Writing Scientific Research Papers Using Large Language Models

Abstract

The rapid advance of artificial intelligence has endowed Large Language Models (LLMs) with formidable capabilities in natural-language generation, textual comprehension and knowledge integration. Consequently, these systems are increasingly deployed to support scholarly paper writing. Prompts—the principal interface between user and model—exert a decisive influence on the quality and usability of the generated text. This study systematically examines design principles, strategies and optimization techniques for prompts employed in LLM-assisted academic writing, and evaluates their effectiveness across discrete research phases through illustrative cases. Findings indicate that prompts characterized by clear structure, sufficient context and unambiguous task specification significantly enhance the logical coherence and disciplinary professionalism of model output. Nevertheless, LLMs remain prone to knowledge hallucination and citation distortion; human verification and ethical safeguards are therefore indispensable. The paper proposes an operational framework for prompt engineering that can guide researchers and foster the responsible integration of AI into academic writing.