# AI-Supported EUD for Data Visualization: An Exploratory Case Study
## 4 Phase 2: Answering RQ2
### Results
#### 4.3.3 Findings for the Advanced User  
Finally, additional themes associated with the advanced user were identified.  
**Effectiveness and efficiency of human-AI interaction.** During the interaction with the system, the advanced user always yielded precise and detailed results, often testing thesystem capability to interpret datasets accurately and demanding greater transparency in data representation. The focus of the interaction soon extended beyond basic interactions, aiming to optimize a workflow through advanced data viz requests for correlation analysis. When requesting correlations between measures, the system sometimes generated an incorrect scatter plot, leading the user to make request formulations more intricate to try to yield the expected result. The system efficiency was appreciated in its capacity for reducing the time spent on manual Excel processing, recognizing its potential for streamlining data analysis and decision making (\"_I'd normally spend half an hour doing this in Excel, but here I can see it instantly_\"). Using the system was found to be a notable improvement in daily work, especially in saving time (\"_It saves me a lot of time, especially when I need to perform monthly comparisons. This also spares me from building pivot tables every single time_\").  
**Retaining human control over the AI-based system.** With good skills in prompt engineering, this user emphasized that the system's effectiveness was heavily influenced by how well requests were formulated (\"_I need to be careful with how I phrase the request, otherwise it gives me something different_\"). Prompts tended to be lengthy and complex, reflecting a desire for full control over data viz and system outputs. There was also a need to go beyond data viz and understand how trustworthy the system was in terms of checking the data at the source from the table.  
**Strengths and weaknesses of LLM-based interaction**. The system was found to be able to express needs and answer requests adequately, as seamlessly as interacting with a common chatbot (\"_The experience is like chatting: I talk to it as if it were a person. There's no need to use strange commands_\"). The clarity and understandability were judged quite well, despite the fact that the system did not understand when it had made a mistake, and it was not easy to try to communicate the mistake. Expectations went beyond the system's capabilities, showing a wish to exploit this kind of system for complex analytical needs, which are, however, still difficult to support with this technology.  
**Trustworthiness and transparency of the system.** After thorough testing, the user expressed trust in using the system for work-related tasks, provided that a proper verification of the dataset was conducted before a full reliance on the system could be admitted. While SQL query visibility was initially valued, this user acknowledged that, once trust is established, such transparency may become unnecessary (\"_As long as the results are consistent, I do not feel the need to verify the code every time_\").