Abstract Sarcasm detection is a nuanced challenge in natural language processing, requiring deep understanding of textual and contextual cues. We present Sarcasm-GPT, a large language model-based model that integrates four key components: prompt template generation, retrieval-augmented generation, chain-of-thought generation, and a context fusion module. Together, these modules enrich contextual modeling, enable systematic reasoning, and seamlessly incorporate multimodal information, significantly boosting sarcasm detection accuracy. Extensive experiments, including ablation studies, confirm the complementary contributions of each module and demonstrate substantial performance gains over baselines. Additionally, our findings highlight the importance of context length in balancing interpretive accuracy and computational efficiency. The code is available at Sarcasm-GPT.