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

arXiv: https://arxiv.org/abs/2608.24907 · HTML: https://mlsanity.com/paper/2608.24907/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning.
- Builds on 3 of its 19 references that are in the map.
- First posted on arXiv 21 Jul 2026.

## Placed in

- LLM reasoning / [Clinical and biomedical applications](https://mlsanity.com/lr/clinical-and-biomedical-applications/)

## Builds on (most cited first, 3 of 3)

- [The Prompt Report: A Systematic Survey of Prompt Engineering Techniques](https://mlsanity.com/paper/2406.06608/) (2024)
- [NutriBench: A Dataset for Evaluating Large Language Models on Nutrition Estimation from Meal Descriptions](https://mlsanity.com/paper/2407.12843/) (2024)
- [SEE: Strategic Exploration and Exploitation for Cohesive In-Context Prompt Optimization](https://mlsanity.com/paper/2402.11347/) (2024)

## Abstract (arXiv)

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.