Aspect-based sentiment analysis offers detailed insights by pinpointing specific product aspects in a text that are associated with sentiments. This study explores it through the prediction of quadruples, comprising aspect, category, opinion, and polarity. We evaluated in-context learning strategies using recently released distilled large language models, ranging from zero to full-dataset demonstrations. Our findings reveal that the performance of these models now positions them between the current state-of-the-art and significantly higher than their earlier generations. Additionally, we experimented with various chain-of-thought prompts, examining sequences such as aspect to category to sentiment in different orders. Our results indicate that the optimal sequence differs from previous assumptions. Additionally, we found that for quadruple prediction, few-shot demonstrations alone yield better performance than chain-of-thought prompting.