Principles of requests’ formulation to artificial intelligence technologies as a component of interaction strategies

Importance. In light of the rapid integration of artificial intelligence (AI) systems in both practical and research applications, the formulation of queries assumes a pivotal role in human–machine interaction strategies. This article delves into the realm of Large Language Models (LLMs) and systematically explores effective prompting principles, highlighting their significance in enhancing the accuracy, consistency, and controllability of AI-generated outputs. Our objective is to construct a comprehensive taxonomy of query types within the framework of interaction strategies.

Materials and Methods. To accomplish this, we employ methods of analysis and synthesis of existing theoretical and practical materials related to this subject. These materials are drawn from studies conducted over the past three years, exploring various approaches to optimizing human–machine communication.

Results and Discussion. The findings of the investigation reveal that there exist several types of inquiries at the initial phase of engagement with the model. Following these queries, a dialogue ensues to validate the accuracy of the provided responses. The algorithm for effective interaction with the machine necessitates specific skills that can be evaluated based on specific criteria and the metrics of the obtained response.

Conclusion. Establishing standardization for query generation processes is crucial for ensuring the secure and responsible utilization of AI in large-scale applications. Consequently, the development of interdisciplinary programs focused on crafting strategies for interacting with AI should be prioritized in future research endeavors.