Large Language Models (LLMs) are revolutionizing how software is built, offering the potential to automate code generation. However, getting LLMs to produce high-quality, functional code isn't as simple as typing in a request. LLMs can sometimes generate irrelevant or incorrect code, highlighting the need for careful prompt engineering. This process, akin to giving the LLM detailed instructions, significantly influences the quality of the generated code. Researchers are constantly exploring different prompt techniques, such as providing examples (few-shot learning), encouraging step-by-step reasoning (chain-of-thought), assigning the LLM a specific role (persona), specifying the function's structure (signature), and listing relevant packages. A recent study delved into the impact of these prompt techniques on code generation, examining how they affect the correctness, similarity to human-written code, and overall quality of the output. The results reveal fascinating insights into the nuances of LLM behavior. While providing a function signature or examples significantly improves the correctness of the generated code, simply combining all available techniques doesn't guarantee the best results. Interestingly, there's a trade-off: some techniques improve correctness but might lead to less maintainable code with more errors. For example, while providing the function signature or examples resulted in more correct code, these also led to more complex functions and sometimes even introduced code smells. On the other hand, using chain-of-thought, persona, or package information improved the quality of the code by reducing smells, but slightly lowered the passing rates of tests. This suggests a need to carefully consider the purpose of code generation when choosing prompt techniques. Different LLMs also react differently to these techniques, emphasizing the importance of tailoring prompt strategies to specific models. While prompt engineering plays a vital role in harnessing the power of LLMs for code generation, the research suggests a nuanced approach. Instead of blindly combining techniques, developers should prioritize the specific needs of their task and the unique characteristics of their chosen LLM. This research paves the way for more advanced prompt programming techniques and underscores the exciting potential of LLMs in shaping the future of software development.