## 4 Discussion
This is because the process of the transmission of information depends on many other factors, for example, the type and quality of the model used of the described building, the complexity of the structure of the space inside the building, the personal characteristics of the recipient and the context of the conditions in which the descriptions are read. The developed application, which can support further research, has proven to be fully useful and will allow the target study to be carried out. The conducting of this survey is currently in the preparatory stage. Such a study and analysis of the results significantly exceed the possibilities of presentation in a single article. The study will give additional information to further assess the quality of geo-description generation with the help of AI methods. At this stage, only a high semantic correspondence between the descriptions generated by the expert and AI has been demonstrated. However, a more complex issue is assessing the correctness of potential users' reading and understanding. This requires, among other things, simultaneous evaluation of the quality of both expert-generated and NLP descriptions. The research that has been carried out can be continued on many levels. In order to provide a more holistic evaluation of the generated geo-descriptions, the authors of this article will additionally consider the use of the BLEU (Bilingual Evaluation Understudy) [33] metric in further research. BLEU is a metric that is widely used to evaluate natural language processing systems, particularly machine translation and natural language generation systems. BLEU takes into account the specific form of a text because it compares common n-grams in the texts being compared. In addition, BLEU takes into account the length of the generated text, which can be useful to avoid favoring geo-descriptions that are too short or too long. After the initial demonstration of the usefulness of large language models in the context of generating geo-descriptions, the rule base for generating phrases describing space should be expanded, taking into account different types of input data (e.g., building type) and different user groups (e.g., blind, visually impaired, young, middle-aged, elderly, women, men, technical worker, firefighter, policeman, client of shopping center). The type of building for the proposed method of using LLMs should not affect the obtained results. The difference should be in the method of performing the GIS analysis in the first phase of the process, and not in the issue of analyzing the use of NLP to create geo-descriptions, which is the essence of this article. However, the type of recipient is of key importance in the context of formulating a geo-description. Different types of users need different information and different linguistic formulations (short, technical, colloquial, funny). In this phase of the research, however, we focused on ordinary users of a common navigation application. In addition to GIS techniques, phrases from spatial data could also be generated automatically using visual analysis methods, which are becoming increasingly popular. An example is the map reading and analysis system proposed by [34]. A similar system could be tested in the context of automatically extracting phrases from maps of building interiors. A significant problem, in this case, could be the shortcomings observed by the authors of the above article in the precision of responses from the model, which is crucial in generating correct geo-descriptions. Nevertheless, given the rapid pace of development of this technology, this could be a direction with much potential. The research carried out assumed testing the zero-shot prompting technique in the process of generating geo-descriptions. However, it is known that in many applications, the use of a single or few examples for the LLM model gives much better results while not involving a lot of work or the need for more calculations.