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Over the past two years, large language models (LLMs) have significantly transformed the daily work of experimental and detector physicists. During this period, we have moved from using conventional chat-based interfaces (ChatGPT, Gemini, etc.) primarily for code development and optimization to employing AI agents (Claude Code, Codex) for the automation, management, and control of experimental setups and measurement procedures, as well as for in-depth analysis and interpretation of experimental results. This presentation will briefly introduce several practical examples of how AI/LLM tools have been integrated into our research activities, highlighting their impact on our workflows, efficiency, and overall approach to experimental work.