In today’s digital age, the huge amount of abusive content and hate speech on social media platforms presents a significant chal- lenge. Natural Language Processing (NLP) methods have focused on detecting explicit forms of hate speech, often overlooking more nuanced and implicit instances. To address this gap, our paper aims to enhance the detection and understanding of implicit and subtle hate speech. More precisely, we propose a comprehensive approach combining prompt con- struction, free-text generation, few-shot learning, and fine-tuning to gen- erate explanations for hate speech classification, with the goal of provid- ing more context for content moderators to unveil the actual nature of a message on social media.