Knowledge-enhanced large language models for ideation to implementation: A new paradigm in product design

Abstract

Traditional product design processes often struggle to accurately capture complex user needs and generate market-relevant solutions due to a heavy reliance on subjective human input and limited decision support tools. While Large Language Models (LLMs) have shown proficiency in various domains, their application in product design remains limited, often resulting in generic outputs. To address this, we propose an innovative paradigm for intelligent product design generation, termed ProdGen. The core of ProdGen is the ProdGen-Agent system, which integrates LLMs with customized expert design tools, leveraging the proposed Multi-Design Task Adapter (MDT-A) method and a Dual Knowledge Enhancement Mechanism. The MDT-A method injects multimodal design task knowledge into LLMs through a unified knowledge fusion framework, enabling enhanced task decomposition and efficient interaction with custom design tools. The Dual Knowledge Enhancement Mechanism enriches LLM performance by incorporating domain-specific knowledge bases and structured graph-based data retrieval, ensuring more accurate and relevant design outputs. Demonstrated through kitchen design cases, ProdGen-Agent autonomously handles the entire design process, excelling in user need analysis, task breakdown, decision-making support, tool integration, and multidimensional design generation. Expert evaluations validate ProdGen-Agent’s effectiveness in automating complex design tasks, confirming its potential to revolutionize product design processes across various industries by leveraging LLMs in combination with domain expertise.