BioMeter and BioManner: Evaluating and enhancing reliability of Large Language Models in bioprocess engineering

The rapid digitalization of bioprocess engineering has stimulated interest in large language models (LLMs) as interactive decision-support tools. Although LLMs encode extensive biological and technical knowledge, their reliability in authentic bioprocess engineering scenarios, where operational constraints, multi-scale dynamics, and process trade-offs must be considered, remains largely unexamined. To address this gap, we introduce BioMeter, a domain-specific evaluation framework constructed from authentic bioprocess engineering problem-solving scenarios, including troubleshooting, optimization, and operational decision tasks. BioMeter enables systematic assessment of whether LLMs can generate operationally feasible and constraint-consistent responses. Comprehensive evaluation of state-of-the-art open-source and commercial LLMs reveals substantial limitations in process-level reasoning, particularly in knowledge integration and decision-making under dynamic constraints. Building on these findings, we propose BioManner, an enhancement framework that improves engineering reliability by integrating hierarchical domain knowledge through retrieval-augmented generation and employing structured reasoning for constraint-aware problem decomposition. Experimental results demonstrate that BioManner significantly improves response accuracy, consistency, and engineering relevance across multiple task types, achieving up to 87% accuracy improvement over baseline models while maintaining full relevance to practical engineering queries. The code and data are available at: https://github.com/TrustMATIC-Lab/BioMM .