The increasing volume of online reviews presents challenges for consumers seeking concise and meaningful insights. While platforms like Amazon employ proprietary AI-driven summarization methods, their lack of transparency creates a gap for other platforms looking to implement similar solutions. This study develops a zero-shot prompt engineering approach for automated review summarization, leveraging AI-driven prompt refinement to generate summaries that align with online platforms’ proprietary outputs. To assess effectiveness, we employ cosine similarity with GloVe embeddings to compare AI-generated summaries and Amazon’s ground-truth summaries' semantic alignment with raw customer reviews. Our results indicate that the AI-refined summaries closely align with Amazon’s summaries in similarity to raw reviews, demonstrating the viability of a transparent and adaptable summarization method. This research contributes to the growing body of IS research on AI-enabled decision support by showcasing a prompt engineering framework that reduces the need for ground-truth training data while maintaining high accuracy and readability.