On Opportunities and Challenges of Large Language Models and GPT for Problem Solving and TRIZ Education

The advent of GPT has caused a real revolution in many application contexts. Even the TRIZ community has had to face up to this new technology, questioning the possible integrations with traditional paths and tools. Many problem-solving experts have for some time been proposing specific prompts based on the methodology’s tools such as functional analysis, reconstruction of cause-effect relationships, identification of Resources, 40 inventive principles, etc., in order to support the problem solver, or even replace him altogether, during the inventive process. The free generation of LLM content has been applied for very different purposes such as, for example, to contextualize general purpose heuristics in specific domains, or as a search engine to answer technical questions, to suggest creative ideas or improve the formulation and redefinition of a problem, or finally to find connections between different application contexts. This article proposes a critical analysis of the real effectiveness of these prompts according to the different needs of users. The analysis was carried out using a software application that was developed in-house and for which a testing phase was conducted on a variegated sample covering both the academic and industrial fields, with more experienced users and users who have been approaching TRIZ for less time.