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Prompting Strategies for Large Language Models: A Structured Review of Techniques, Applications, and Open Challenges
编号：39 访问权限：仅限参会人 更新：2026-07-22 16:09:19 浏览：0次 Online

报告开始：2026年07月30日 18:05（Asia/Kolkata）

报告时间：15min

所在会场：[S6] Artificial Intelligence Use Cases [S6-2] Artificial Intelligence Use Cases

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摘要
Prompting strategies for large language models, including chain-of-thought reasoning, few-shot demonstrations, instruction tuning, and automatic prompt optimisation, have become central to how practitioners extract useful behaviour from these systems without modifying model weights. Recent growth in research on these methods has produced a wide and fragmented body of work that spans reasoning benchmarks, medical applications, code generation, and multilingual settings. This paper presents a comprehensive review of sixty-three studies published between 2021 and 2025, covering commonly examined technique categories, datasets, evaluation approaches, and reported limitations. Stepwise reasoning formats, in-context learning, zero-shot instruction following, soft prompt tuning, and agent-style iterative prompting have each shown meaningful improvements over baseline approaches across diverse task types. A systematic review process is used to compare performance trends and surface recurring limitations such as output sensitivity to phrasing, the absence of standardised benchmarks, prompt injection vulnerabilities, and limited multilingual coverage. The study draws attention to future directions including security-aware prompting, cross-lingual adaptation, explainable prompt analysis, and unified evaluation infrastructure. Taken together, the reviewed work demonstrates that prompt engineering has matured into a field with genuine technical depth, though several foundational problems remain unresolved.   
 
关键词
Prompt Engineering, Large Language Models, Chain-of-Thought Prompting, Few-Shot Learning, Instruction Tuning, Automatic Prompt Optimization, Agentic Prompting, Natural Language Processing
报告人
Heera Patwal
Education Graphic Era Hill University

稿件作者
Heera Patwal Graphic Era Hill University
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重要日期

  • 会议日期

    07月30日

    2026

    至

    08月01日

    2026

  • 06月30日 2026

    初稿截稿日期

  • 07月30日 2026

    注册截止日期

主办单位
The United Societies of Science
承办单位
Kongunadu College of Engineering and Technology
协办单位
IEEE Section
IEEE Madras Section
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