Domain Adaptation of Large Language Models for Polymer-Composite Additive Manufacturing Using Retrieval-Augmented Generation and Fine-Tuning

This study explores strategies for adapting general-purpose large language models (LLMs) to specialized engineering domains, specifically additive manufacturing, to enhance answer accuracy and relevance. It investigates the use of domain-specific fine-tuning and retrieval-augmented generation (RAG) by constructing a curated corpus for evaluation.