Explaining dietary CO₂ impact with trustworthy agentic LLMs, ML, and XAI

Large Language Models (LLMs) offer transformative capabilities for scientific workflows, enabling scalable analysis, evidence synthesis, and insight generation. We present an agentic LLM workflow, applied to the EU-funded SWITCH project, which investigates the environmental impact of dietary choices, including CO₂ emissions associated with food consumption.

Validated questionnaire responses from SWITCH participants are securely anonymized and processed using machine learning methods, including clustering and classification, interpreted using ExaplainableAI (XAI) to ensure transparency of feature contributions, to generate food behavioral profiles, including nutritional and environmental habits, while preserving individual privacy. These outputs guide the construction of structured agent directives, enriching contextual information and constraining the LLM to provide scientifically grounded answers and data-driven insights.

Responses are generated within a Retrieval-Augmented Generation (RAG) framework over a curated Data Lake of revisioned documents, including project deliverables, scientific reports, and nutrition-environment datasets covering sustainable diets, CO₂ emissions, and European food policy. The combination of ML-generated profiles and the RAG context acts as a set of constraints, ensuring that LLM outputs remain traceable, grounded, and aligned with verified evidence.

Human-in-the-loop review ensures the quality and correctness of the ML-generated profiles, the construction of agent directives, the LLM outputs, and the revisioned documents used in the RAG framework, while metadata and traceability mechanisms ensure auditability, reproducibility, and risk mitigation.

Our results demonstrate that combining classical machine learning, structured agent directives guided by clustering and classification, RAG grounding, metadata and traceability, and human oversight enables trustworthy, effective, and transformative scientific analysis, highlighting the potential of agentic LLMs for scalable, insight-driven applications in research while ensuring responsible AI deployment.