We investigate the effects of various hints including an introduction text, a few examples, and prompting techniques to enhance the performance of a Large-Language Model (LLM) in detecting a spoiler review of a movie. Detecting a spoiler review of a movie represents an important Natural Language Processing (NLP) task which resists the Deep Learning (DL) approach due to its highly subjective nature and scarcity in data. The highly subjective nature is also the main reason of the poor performance of LLMs-based methods, which explains their scarcity for the target problem. We address this problem by providing the LLM with an introduction text of the movie and a few reviews with their class labels as well as equipping it with a prompt that selects and exploits spoiler types with reasoning. Experiments using 400 manually labeled reviews and about 3200 LLM-labeled reviews show that our CAST (Clue And Select Types prompting) outperforms (0.05 higher) or is on par with (only 0.01 lower) cutting-edge LLM-based methods in three out of four movies in ROC-AUC. We believe our study represents an evidence of a target problem in which the knowledge intensive approach outperforms the learning-based approach.