RAG-Enhanced Sustainable Product Design for Circular Economy Transition
Sustainable product development is central to achieving the goals of a circular economy, where waste is minimized, and resources are optimized throughout the product lifecycle. Traditional design processes often require specialized sustainability expertise and rely on tools introduced at later stages of development. However, integrating sustainability at the conceptual stage can lead to more innovative and impactful outcomes. Large Language Models (LLMs), such as ChatGPT, have demonstrated strong potential in assisting designers through knowledge retrieval and idea generation. Yet, their generality and ambiguity often reduce practical relevance. This study introduces a retrieval-augmented generation (RAG) framework tailored for sustainable product design, ensuring more accurate, context-driven, and actionable design insights.
The Role of Design in Circular Economy
Design is widely recognized as the driving force behind sustainable innovation, acting as the foundation for product performance, desirability, and lifecycle impact. In the circular economy paradigm, design decisions directly influence resource efficiency, product longevity, and reuse potential. Embedding sustainability principles at this stage can reduce environmental impact and enable businesses to adapt to global sustainability goals. By empowering designers with knowledge-driven solutions, product development can move beyond incremental improvements toward transformative change.
Limitations of Current LLM Approaches
While LLMs are valuable for generating design concepts, their reliance on generalized training data leads to vague or superficial recommendations. This limits their applicability in technical contexts such as sustainability, where designers need actionable and context-specific guidelines. Without integration of external knowledge sources, LLMs often overlook key sustainability considerations, resulting in incomplete or impractical outputs. Therefore, a more structured framework is essential to overcome these limitations.
RAG-Based Framework for Sustainable Design
The proposed two-stage RAG framework enhances sustainable product development by combining generative reasoning with domain-specific information retrieval. Two external knowledge bases support this framework: one focused on systems design principles and another on sustainable design strategies. By embedding these sources, the framework delivers precise and relevant guidelines that align seamlessly with the design thinking process. Unlike traditional approaches, this method requires minimal prior sustainability expertise, making it more accessible to designers across industries.
Case Study Findings and Performance Improvements
Expert-evaluated case studies validate the effectiveness of the RAG-based framework, demonstrating a 2.7-fold increase in relevant product design specification coverage compared to non-RAG methods. The framework particularly excelled in enhancing product Desirability and addressing Use-stage lifecycle considerations, both of which are often neglected in traditional sustainability assessments. Improvements were also observed across other performance metrics, showcasing the framework’s ability to produce holistic and practical design recommendations.
Research Implications and Future Directions
This research highlights the transformative potential of embedding sustainability considerations at the conceptual stage of design through advanced AI frameworks. The low implementation cost and scalability of the RAG approach make it a viable tool for accelerating the adoption of circular economy principles. Future studies may focus on expanding knowledge bases, integrating real-time data, and refining evaluation metrics to further enhance decision-making. Ultimately, this work paves the way for designers to innovate sustainably and contribute to systemic change in product development.
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