Large language models (LLMs) are reshaping radiology through their advanced capabilities in tasks such as medical report generation and clinical decision support. However, their effectiveness is heavily influenced by prompt engineering—the design of input prompts that guide the model’s responses. This review aims to illustrate how different prompt engineering techniques, including zero-shot, one-shot, few-shot, chain of thought, and tree of thought, affect LLM performance in a radiology context.