Toward an integrated animal-human nutrition intelligence via agentic retrieval-augmented language models.

Animal-source foods are nutritionally "plastic," with fatty acid profiles, vitamins, and minerals that can be altered by animal diets and management. Although the literature documenting these effects is extensive, it is fragmented across disciplines and experimental contexts, limiting translation into actionable guidance for food quality and human nutrition. We developed the Intelligent System for Integrating Global Human & Animal Health Technology (INSIGHT), a domain-specialized retrieval-augmented generation (RAG) system designed to synthesize evidence across animal production and human nutrition research with explicit provenance. INSIGHT employs a nine-stage RAG pipeline that integrates query expansion, hybrid retrieval, evidence reranking, and self-verification to deliver transparent, citation-linked responses. Throughout, retrieval refers to document selection, integration to the combination of retrieved evidence into a coherent evidence set, and synthesis to the LLM-based generation of grounded narrative responses. The knowledge base comprises ~4,000 peer-reviewed papers in animal science, feed composition, and human dietary research. To evaluate retrieval performance across diverse literature contexts, we developed a multi-group evaluation framework: 282 documents were randomly selected and organized into 26 semantically coherent groups of ~10 papers each. For each group, Perplexity Deep Research generated 25 question-answer pairs and identified ground-truth relevant documents. Each question was posed to INSIGHT, yielding document-level precision, recall, and F1-score metrics across 614 total queries. Generalized linear mixed models with beta regression revealed significant between-group performance variation (