The authors built and expert-evaluated an agentic AI system integrating DEA regulatory data with dynamic scientific literature via RAG to provide accurate, context-sensitive substance use education, with mean Likert ratings of 4.18-4.35 and substantial rater agreement.
Retrieval-augmented generation (RAG) and LLMs for enterprise knowledge management
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ChipLingo trains LLMs on EDA data via corpus construction, domain-adaptive pretraining, and RAG scenario alignment, reaching 59.7% accuracy with an 8B model and 70.02% with a 32B model on a new internal EDA benchmark.
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Agentic AI for Substance Use Education: Integrating Regulatory and Scientific Knowledge Sources
The authors built and expert-evaluated an agentic AI system integrating DEA regulatory data with dynamic scientific literature via RAG to provide accurate, context-sensitive substance use education, with mean Likert ratings of 4.18-4.35 and substantial rater agreement.
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ChipLingo: A Systematic Training Framework for Large Language Models in EDA
ChipLingo trains LLMs on EDA data via corpus construction, domain-adaptive pretraining, and RAG scenario alignment, reaching 59.7% accuracy with an 8B model and 70.02% with a 32B model on a new internal EDA benchmark.