## 4 Results
### Results and Interpretations  
First, the histograms in Figure 4 illustrate the distribution of grade differences across all query categories included in the analysis. Each category is represented by a pair of histograms: blue bars indicate the grade differences for prompts that were not assigned to the given category, while red bars show the grade differences for prompts that were assigned to that category. These visualizations provide an initial impression of the impact that different query types may have had on the grading outcomes. In some cases (especially EF and CU), noticeable differences between the red and blue distributions suggest potential effects of category membership. However, we conducted formal statistical analyses to move beyond visual interpretation and rigorously assess whether these observed differences are statistically meaningful or simply the result of random variation. This approach allows us to determine whether the patterns seen in the histograms reflect genuine relationships in the data. Figure 3: Mean number of queries per category: Tagged vs. untagged conditions. The chart shows how tagging affects query frequency across categories. A notable decrease in Error Fixing (EF) and Code Understanding (CU) queries when tags are present suggests that students formulated more precise prompts, potentially improving the effectiveness of LLM responses. This study employed the Mann-Whitney U test to evaluate the differences in student performance based on the types of queries made to the LLM. This non-parametric test was selected due to the non-normal distribution of the data, as indicated by the Shapiro-Wilk test (\(p<0.05\)). The test provided a robust method for assessing whether specific query categories significantly influenced students' coding performance. The analysis focused on the relationship between query types and two primary outcomes: the production of runnable code and overall assignment performance. The following query categories were analysed: Feature Implementation (FI), Best Practices (BP), Documentation (DOC), Concept Clarification (CC), Error Fixing (EF), Code Understanding  
Figure 4: Distribution of grade differences by query category. Each histogram pair contrasts grade outcomes between students whose queries fell into a given category versus those whose queries did not. Red bars indicate tagged queries, while blue bars indicate untagged ones. Notable shifts for Error Fixing (EF) and Code Understanding (CU) suggest a measurable performance gain linked to precise, well-categorized prompts. (CU), and Code Optimization (CO). Results were divided into tagged and untagged queries to investigate the impact of categorization on performance. The statistical results for each query type are summarized in Table 2 below. These results highlight the U-values and \(p\)-values obtained from the Mann-Whitney U test, indicating the significance of differences in performance outcomes between tagged and untagged queries. The results show that two categories, EF and CU, exhibited statistically significant differences. For EF, the Mann-Whitney U statistic was 135.50, with a \(p\)-value of 0.042, indicating that tagged queries in this category significantly improved students' ability to produce runnable code. Similarly, for CU, the Mann-Whitney U statistic was 105.00, with a \(p\)-value of 0.030, suggesting that tagged queries focusing on understanding code concepts led to better performance outcomes. These findings emphasize the importance of precise and deliberate tagging [21] in improving the effectiveness of LLM interactions. This pattern aligns with recent findings in prompt engineering research. As shown in the work by [22], high-specificity prompts, those that include clear instructions, constraints, and objectives, tend to generate more accurate and relevant LLM outputs, whereas low-specificity prompts introduce ambiguity that can lead to irrelevant or erroneous completions.