Strategic Potential of Patent-Based RAG Systems for Industrial R&D Applications: A Comparison with General-Purpose LLMs

Large language models (LLMs) have rapidly transformed how information is accessed and generated across domains by leveraging deep learning to produce human-like responses. Their applications have become powerful in supporting coding, law, medicine, and design tools. However, despite their capabilities, LLMs often suffer from critical limitations such as hallucinations, lack of domain specificity, and reliance on generalized internet-based knowledge. These limitations could pose risks for industrial research and development (R&D), where precision and innovation are essential. This study investigates the potential of a patent-based Retrieval-Augmented Generation (RAG) tool (Omnia) to support R&D activities more effectively than general-purpose LLMs (Google AI Studio). Omnia accesses patent databases in real-time to provide structured and validated data, offering reliable and domain-specific responses. Multiple research questions were generated to evaluate the responses from both Omnia and Google AI Studio, which are addressed through targeted case studies. Findings demonstrate that patent-based RAG systems can offer significant advantages in R&D scenarios, including semantic search accuracy, TRIZ-based problem-solving, technical failure analysis, prospective life cycle assessment, and identification of circular economy opportunities.