Using the validation set, we conducted a grid-search to find the best hyperparameters. Afterwards, we finetuned the base LLM independently using the three training datasets (brute-force, manual-based, rule-based), and probed the impact of RAG and prompt engineering on model performance. These three techniques were used with GPT-3.5 Turbo in all possible configurations. Additionally, GPT-4o, which, at the time of writing, could not be finetuned yet, was used in its standard configuration as a strong baseline.