ChatUsing generative AI for academic research often resulted in fabricated citations and superficial paper summaries; structured prompt engineering frameworks enable reproducible, hallucination-resistant scientific literature mapping.
The exponential growth of biomedical literature leaves researchers drowning in tens of thousands of papers for every scoping review, prompting intense interest in leveraging large language models to accelerate synthesis.
Unconstrained ChatGPT queries frequently suffer from plausible hallucinations, inventing synthetic DOIs, misattributing author claims, and missing critical exclusion criteria in clinical systematic reviews.
This methodology establishes a structured prompt engineering protocol—incorporating role assignment, few-shot contextual boundaries, chain-of-verification checks, and automated extraction templates—demonstrated across a comprehensive healthcare case study.
This practical guide establishes a standard operating procedure for AI-assisted literature discovery, empowering academic researchers to synthesize vast evidence bases rapidly while safeguarding scientific rigor and reproducibility.
Prompt engineering in ChatGPT for literature review: practical guide exemplified with studies on white phosphors
Recent advancements in large language models (LLMs) such as ChatGPT have been transforming the ways we approach science tasks, including data analysis, experimental design, writing, and literature review. However, due to the lack of specialized knowledge and inherent issues such as plagiarism and hallucinations (i.e., false or misleading outputs), it is necessary for users to verify the output information. To address these issues, prompt engineering has become a significant task. In this study, we evaluate the performance of different prompt styles for extracting information from literature abstracts and emphasize the importance of prompt engineering for such scientific tasks. The literature on white phosphor materials is used for this study due to the availability of important and quantitative information in the abstracts. Through detailed comparative and quantitative evaluation, we provide guidance on preparing suitable and effective prompts based on the types of information sought. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-025-99423-9.
Giant laser observatories like LIGO are blinded at low frequencies by the rumbling vibrations of Earth's seismic waves; dropping clouds of ultra-cold strontium atoms in a ten-meter vacuum tower allows quantum wave interference to measure gravitational ripples with zero seismic interference. Published in Nature, this prototype differential atom interferometer opens the uncharted "mid-band" gravitational wave frequency window, creating an ultra-sensitive quantum sensor to hunt for ultralight dark matter.
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