Формалізація текстових запитів до систем штучного інтелекту

This study's object is the process that formalizes text prompts to large language models of artificial intelligence for the purpose of automatically generating action cards for hexagonal tabletop wargames. The task relates to the ambiguity (42% of terms are misinterpreted), contextual incompleteness (37%), and syntactic variability (21%) of natural language prompts, resulting in unhelpful and unpredictable responses. To address this problem, a conceptual-practical model has been proposed, which combines structured prompt templates, a localized glossary of key terms, as well as clear instructions on response format. Practical verification was carried out by generating a set of action cards for solo wargames on modern large language models by generating over 100 prompts and analyzing more than 300 responses. The experiments demonstrated that the formalized prompts reduced the total error rate by 58%, as well as increased the relevance of responses from 55–65% to 88–92%. The average time for preparing prompts was reduced by 25–40%. The “d6-table” templates ensured the stability of the output format in 90–95% of cases while JSON structures provided stability in 85–90% of cases. The glossary and structure definition integrated into the prompts minimized semantic discrepancies and syntactic errors. A special feature of the proposed template structures for prompts is adaptability to different subject areas through the use of a description language specific to each of these areas. The research results have practical value for automating game content development processes and could be adapted for other subject areas where accuracy, consistency, and structure of language model responses are important. The proposed systematic approach facilitates the automation of complex content development with a guaranteed increase in the quality and predictability of responses from large language models.