## 5 Discussion
### Main Analysis: Alignment, Improvement, and Generation
#### 5.1.4 Guidelines for Agent Selection  
Table 3 converts these findings into a practical reference. Each row captures an intersection of (i) [PERSON]'s category (Knowledge-Based, Application-Based, Creation and Evaluation), (ii) question design (single-step or multi-step), and (iii) course orientation (theory-oriented or application-oriented). For each combination, the table identifies which agent tended to show higher performance in Task 1 (Alignment Evaluation), Task 2 (Question Improvement), and Task 3 (New Question Generation). When no consistent leader emerged, we note \"All three agents performed similarly\" to reflect minimal or inconsistent differences among the fine-tuned LLM, VectorRAG, and VectorGraphRAG.  
While the Fine-Tuned LLM frequently exhibits strong performance in theory-oriented, multi-step tasks across all three tasks, VectorRAG tends to excel in application-oriented conditions for Tasks 2 and 3. Rows labeled \"All three agents performed similarly\" indicate  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Question** & & & **Higher** & **Higher** & **Higher** \\
**Category** & **Question Design** & **Course Category** & **Performance** & **Performance** & **Performance** \\  & & & **(Task 1)** & **(Task 2)** & **(Task 3)** \\ \hline Knowledge-Based & Single-Step & Theory-Oriented & Fine-Tuned LLM & Fine-Tuned LLM & Fine-Tuned LLM \\ \hline Knowledge-Based & Single-Step & Application- & & & All three agents & All three agents \\  & & Oriented & VectorRAG & performed & performed \\  & & & & similarly * & similarly * \\ \hline Knowledge-Based & Multi-Step & Theory-Oriented & Fine-Tuned LLM & Fine-Tuned LLM & Fine-Tuned LLM \\ \hline Knowledge-Based & Multi-Step & Application- & & All three agents & & \\  & & Oriented & performed & & \\  & & & & & \\ \hline Application-Based & Single-Step & & & & \\  & & & & & \\ \hline Application-Based & Multi-Step & & & All three agents & & \\  & & & & performed & \\  & & & & & \\ \hline Application-Based & Multi-Step & & & & \\  & & & & & \\ \hline Creation and & & & & & \\ Evaluation & Single-Step & & & & \\ \hline Creation and & & & & & All three agents & All three agents \\  & & & & & performed \\  & & & & & \\ \hline Creation and & & & & & \\ Evaluation & Multi-Step & & & & \\ \hline Creation and & & & & & \\ Evaluation & Multi-Step & & & & \\ \hline Creation and & & & & & \\ Evaluation & Multi-Step & & & & \\ \hline Creation and & & & & & \\ Evaluation & Multi-Step & & & & \\ \hline \hline \end{tabular}  
* Fine-Tuned LLM, VectorRAG, and VectorGraphRAG showed no significant or consistent difference in performance under those conditions.  
\end{table}
Table 3: Observed performance patterns per task under each question category \(\times\) question design \(\times\) course category combination. “All three agents performed similarly” indicates no pronounced advantage for a single agent.  
that neither fine-tuned LLM nor VectorRAG nor VectorGraphRAG consistently outperformed the others, typically reflecting subtler differences that were not clearly resolved by the model predictions.  
These findings suggest a task-agent matching approach, with a fine-tuned LLM for conceptual depth, VectorRAG for simpler application-based tasks, and VectorGraphRAG for cross-topic or multi-hop contexts. Certain agents perform better in specific scenarios because their design and training align differently with the nature of the question. The fine-tuned LLM, having absorbed domain-specific patterns from curated datasets, typically excels in multi-step theoretical tasks that require deeper conceptual reasoning. VectorRAG, which dynamically retrieves context from a vector database, tends to outperform others in practical or single-step prompts where references to real-world frameworks or coding snippets are essential. Meanwhile, VectorGraphRAG, enriched by a knowledge-graph layer, can handle multi-topic or cross-linked tasks effectively when it harnesses its graph-based insights and produces sufficiently cohesive explanations.