PA-CoT: Profile-Adaptive Chain-of-Thought for Personalized Nutritional Consulting

Evgenii Garmashov, Khairullin Artur, Kulin Nikita, Zhuravlev Viktor, Sergey Muravyov, Daniil Sukhorukov, Mikhail Mozikov, Ilya Makarov

Keywords: personalization, prompt engineering, chain-of-thought, health, LLM, nutrition

TL;DR: PA-CoT adds a dedicated profile-analysis stage to LLM prompting, achieving top personalization and safety scores among 12 methods on a new nutritional consulting benchmark.

Abstract: In health and nutrition consulting, widely used prompting methods pass the user profile as an unstructured block without a dedicated analysis step, leaving personalization as a critical structural gap. We introduce PA-CoT (Profile-Adaptive Chain-of-Thought), a multi-stage prompting method that treats profile interpretation as an explicit, standalone reasoning step prior to response generation. To enable systematic evaluation, we introduce the QPA (Question–Profile–Answer) benchmark—200 nutritional consulting samples with structured user profiles scored on four criteria. In a comparative study against 11 comparison methods (CoT, Few-Shot, Role Prompting, DSPy, TextGrad, Self-Refine, and others, plus a Zero-Shot Baseline; 12 total including PA-CoT), PA-CoT achieves the best average score (4.21 on the G-Eval 1–5 scale) and leads on both Personalization (4.71 vs. 4.39) and Safety (4.68 vs. 4.52) with non-overlapping 95% confidence intervals over the nearest competitor—the only method to simultaneously top both criteria. The results confirm that an explicit profile-analysis step is the key driver of personalization gains over widely used prompting approaches.

Submission Number: 89