## 4 Experiments and Results
### Experiments
Surprisingly, the results showed that no model improved with its specific prompt compared to the standardized one, with some models even showing slight declines in validity and contextual understanding. Zephyr maintained its quality, while all other models performed worse, especially in terms of validity. However, the scores still ranged between 3.5 and 5, maintaining a fairly good overall response quality. This finding underscores that complex prompt customization does not necessarily lead to better results for GDPR-related tasks. Instead, simplicity and clarity of prompts may lead to better results across models, suggesting that optimization of prompt design should focus on transparency rather than pretraining alignment alone. **Experiment 3--few-shot prompting**. Experiment 3 tested the effect of providing context to the model through prompting. Contrary to our expectations, few-shot prompting had no significant effect on answer quality. Most models maintained high scores in validity and contextual understanding, although GPT-4 showed slightly better contextual accuracy than the open-source models. Interestingly, Qwen2-7B outperformed GPT-4 in certain aspects of answer validity, suggesting that they may be able to align with specific data categories under GDPR requirements. This suggests that open-source models can effectively use context in compliance tasks, even if few-shot prompting does not significantly impact overall performance. For instance, the ability of Qwen2-7B to align with GDPR data categories highlights its utility in data subject access requests, where accurate and contextual retrieval of structured information is critical. However, the slightly better contextual accuracy of GPT-4 suggests it may be better suited for tasks requiring nuanced legal reasoning, such as proportionality assessments under GDPR Article 6 [3]. **Experiment 4--RAG**. In Experiment 4, we extended the context provided to the model during inference using the RAG technique. RAG proved beneficial for tasks directly covered in the source documents, as models accurately retrieved relevant segments from known processing activities. However, RAG struggled with novel data categories that were not present in the source text, resulting in less structured responses from all models. This limitation affects tasks like generating records of processing activities, where precise categorization is necessary to demonstrate compliance with GDPR Articles 5 and 30 [3]. Furthermore, all of the models, including GPT-4, ignored the structured list format, outputting plain text instead of the required list format. These findings indicate that RAG significantly improves performance for known categories by providing relevant context but becomes less effective when dealing with novel categories. In particular, providing the model with the GDPR itself as a source document had no effect. This is due to significant differences between the language in the prompt and the legal phrasing and structure in the GDPR text. These differencesmake it difficult to retrieve contextually useful content to generate records of processing activities. Consequently, the models were unable to infer from the embedded GDPR content, highlighting a limitation in using broad legal documents as reference material for highly specialized tasks. This indicates a need for fine-tuning models on domain-specific datasets, such as annotated GDPR case studies, to enhance their ability to align with legal phrasing and structure. Moreover, an observed tendency of the models to disregard structural instructions suggests that RAG, as implemented here, may not fully support structured outputs in legal document drafting where adherence to format is critical for compliance. The experimental findings offer valuable insights into model performance under specific conditions. To complement this analysis, we present the benchmark results in the following. This provides a broader, standardized evaluation of model capabilities in GDPR compliance tasks.