* **Problem:** LLMs struggle to derive reliable meaning from raw sensor signals, often producing non-actionable or factually incorrect interpretations of time-series data.
* **Methodology:** The study implements a structured RAG-based prompt structure that combines water consumption measurements with descriptive statistics and qualitative user information (such as household water practices).
* **Key Finding:** Augmenting prompts with multidimensional contextual information leads to much higher evaluation scores for grounding, pattern recognition, and actionable recommendations.
* **Methodology:** The study implements a structured RAG-based prompt structure that combines water consumption measurements with descriptive statistics and qualitative user information (such as household water practices).
* **Key Finding:** Augmenting prompts with multidimensional contextual information leads to much higher evaluation scores for grounding, pattern recognition, and actionable recommendations.
