Abstract
Language learning benefits from a comprehensive approach, but traditional software often lacks personalization. This study analyzes prompt engineering principles to implement a test generation algorithm using Large Language Models (LLMs). The approach involved examining these principles, exploring related strategies, and creating a unified prompt structure. A test generation script was developed and integrated into an API for an interactive language learning platform. While LLM integration offers highly effective, personalized learning experiences, issues like response time and content diversity need addressing. Future advancements in LLM technology are expected to resolve these limitations.
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