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Exploring Prompt Engineering: A Systematic Review with SWOT Analysis

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arxiv 2410.12843 v1 pith:VDES6HDD submitted 2024-10-09 cs.CL cs.AI

Aditi Singh , Abul Ehtesham , Gaurav Kumar Gupta , Nikhil Kumar Chatta , Saket Kumar , Tala Talaei Khoei This is my paper · ORCID
classification cs.CLcs.AI
keywords analysisengineeringprompttechniqueslanguageswotaddressingadvancing
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In this paper, we conduct a comprehensive SWOT analysis of prompt engineering techniques within the realm of Large Language Models (LLMs). Emphasizing linguistic principles, we examine various techniques to identify their strengths, weaknesses, opportunities, and threats. Our findings provide insights into enhancing AI interactions and improving language model comprehension of human prompts. The analysis covers techniques including template-based approaches and fine-tuning, addressing the problems and challenges associated with each. The conclusion offers future research directions aimed at advancing the effectiveness of prompt engineering in optimizing human-machine communication.

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  1. AI-Facilitated Analysis of Abstracts and Conclusions: Flagging Unsubstantiated Claims and Ambiguous Pronouns

    cs.CL 2025-06 conditional novelty 4.0 of 10

    Structured prompts can steer LLMs to flag certain unsupported claims and ambiguous pronouns, but performance varies sharply by model, context, and the syntactic role of the target, and the single test case was also th...

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