Utilizing Advanced Prompt Engineering Techniques To Augment Plastic Surgery Postoperative Support

Abstract

PURPOSE: Large language models (LLMs) can offer scalable, conversational guidance for patients recovering from surgery, but are hindered by hallucinationsplausible yet incorrect information that can be harmful if taken as fact. Retrieval-augmented generation (RAG) systems improve factual accuracy and reduce hallucination rates by grounding responses in trusted knowledge sources. However, they still face high nonresponse rates due to retrieval failures and semantic misalignment, limiting their patient-facing utility. This study evaluates whether structured prompting strategiesChain-of-Thought (CoT), Tree-of-Thought (ToT), and Graph-of-Thought (GoT)can overcome these limitations to deliver more accurate, reliable support in rhinoplasty recovery. METHODS: Thirty real-world postoperative patient queries, encompassing both routine and urgent concerns, were tested across four RAG conditions: Standard RAG, CoT, ToT, and GoT. Questions ranged from routine (e.g., When are my sutures and nasal splints removed?) to urgent complications (e.g., My nose is continuously bleeding⋯ What should I do?) and were processed through Gemini 1.0-Pro. Responses were blindly rated by two independent reviewers, with a third resolving disagreements, against chapters from five standard surgical references (e.g., Rhinoplasty Cases and Techniques ). Scoring included accuracy (5-point Likert), comprehensiveness (3-point Likert), readability (Flesch-Kincaid Grade Level, Flesch Reading Ease), understandability (PEMAT), and actionability (PEMAT). Statistical analyses were performed using Friedman tests with Bonferroni-adjusted pairwise comparisons (p<0.008 considered statistically significant). RESULTS: Structured prompting improved system accuracy and reduced nonresponses. ToT achieved the highest accuracy (mean 4.5 vs. 3.4 for Standard RAG, p=0.002) while cutting nonresponses to a single case (3.3% vs. 26.7% for Standard RAG). CoT and GoT also trended toward higher accuracy but did not reach statistical significance after correction. Comprehensiveness scores were similar across all methods. All structured approaches, however, reduced understandability compared to baseline (ToT p=0.001 vs. Standard RAG), while readability remained above the recommended eighth-grade level for patient education. Actionability scores were uniformly low. CONCLUSION: Structured promptingparticularly Tree-of-Thoughtemerges as a key determinant of RAG system performance in surgical aftercare. By reshaping how queries are internally processed, structured prompts mitigate retrieval failures and substantially enhance accuracy without altering the underlying model or data. These gains, however, come at the cost of reduced linguistic accessibility, underscoring the need to pair structured reasoning with human-centered language simplification. Future work should expand validation across broader surgical populations and refine prompting architectures to balance factual precision with clarity and usability. Prompt design is not a marginal adjustment but a central lever for building safe, equitable, and scalable AI-driven patient support systems.© 2026. Plastic Surgery Research Council | All rights reserved |*Source: https://ps-rc.org/meeting/Program/2026/AS46.cgi*

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