This thesis studies the use of LLMs within smart home systems for ambiguity detection and resolution. Existing literature often overlooks the significance of user-centric disambiguation, as smart home systems typically interpret user intentions based on context and predefined settings without considering the user's specific definitions of ambiguous concepts (e.g., "large," "cozy," "comfortable"). This oversight potentially impacts the accuracy of results from the user's perspective. This thesis explores how users perceive the interaction with a text-based smart-home system and its results when user-oriented disambiguation is set in place. To achieve this, an add-on system was developed to identify concept ambiguities in user intents and present disambiguation options in both text and image formats using LLMs and multiple prompting strategies.