The recent, rapid development and popularization of Large Language Models (LLM) have transformed the panorama of Natural Language Processing (NLP) and, more generally, of Artificial Intelligence (AI), permeating into society and transforming the way many tasks are performed, being now either supported or automated with the help of LLM-based tools. Along with the challenges of hallucinations, lack of reasoning capabilities, inability to perform numerical calculations, natural aging of the training data, and improper traceability and citation of information sources, another intrinsic challenge of LLMs, tightly related to their architecture and training, concerns their limited context window and maximum token output (Kaddour et al., 2023). Indeed, the context window is the cornerstone for LLM-based applications which require the previous interactions in the conversation to be preserved and considered by the LLM. This, being true for long conversations, is of particular importance in the engineering design field when an LLM is used to support engineers in the design of a system, going from high-level concept generation to lower-level system requirements or technical specifications generation. This application requires previous decisions as well as the decision-making process to be considered in later stages.

DSM is known for its simplicity and conciseness in representation and exists in the form of a square matrix that maps the relationships between the set of system elements [Yassine and Braha 2003; Browning 2015]. An example DSM (= 4) is shown in Figure 1. Based on the DSM convention described by Browning [2001], Element 1 depends on Element 2 as indicated by a red cell entry in row 2 column 1 of the DSM. Likewise, Element 4 depends on Element 3 as indicated in row 3 column 4. The diagonal of the DSM maps each element to itself and is indicated as black cells in Figure 1. The diagonal is usually left empty but is sometimes used as a space to store element-specific data, such as the likelihood of changing the given element based on market projection [Koh et al. 2013]. The DSM in Figure 1 is not symmetrical across the diagonal, indicating asymmetrical dependencies between the system elements. For example, Element 1 depends on Element 2 but Element 2 does not depend on Element 1. In contrast, the example DSM shows that Element 2 and Element 4 have a symmetrical interdependency. It is important to note that a transposed version of the DSM convention is also widely adopted by many (e.g.