The launch of ChatGPT has sparked significant interest in generative AI, and people are becoming more familiar with the ins and outs of large language models. Understanding the inner workings of this powerful tool requires a deeper look at prompt engineering, which plays a critical role in the success of training such models. Crafting effective prompts is essential to ensure that the model is trained on high-quality data that accurately reflects the underlying task, leading to improved accuracy, increased efficiency, and customizability. Prompt engineering has various industry-specific use cases, including healthcare, finance, and education, where it can be used to personalize learning experiences, provide personalized investment advice, or improve medical diagnosis. To generate effective prompts, one must understand the use case in detail, test and refine the prompts based on the output, and reduce unnecessary information. However, prompt engineering also has limitations and challenges, including bias in prompt generations, difficulty in generating effective prompts, limited flexibility, time-consuming process, lack of generalization, and data privacy concerns, which require careful consideration to ensure responsible use of language models.