AskNatureGPT: an LLM-driven concept generation method based on bio-inspired design knowledge

Concept generation is the early stage in the engineering design process to produce initial design concepts. By applying bio-inspired design (BID) knowledge, designers can employ biological analogies for solution-driven BID concepts. Solution-driven BID starts with knowledge of a specific biological system for technical design. Despite the proven benefits of solution-driven BID, the gap between biological solutions and engineering problems hinders its effective application, with designers frequently encountering misaligned problem-solution pairs and facing multidisciplinary knowledge gaps in concept generation. Therefore, this research proposes a large language model (LLM) based concept generation method – AskNatureGPT – to automatically search for problems, transfer biological analogy, and generate solution-driven BID concepts in the form of natural language. A concept generator and two evaluators are identified and fine-tuned based on the LLM. The method is evaluated by an ablation study, machine-based quantitative assessments, subjective human evaluations, and a case study. The results show our method can generate solution-driven BID concepts with high quality.