The generative AI revolution has made significant progress in the past year, mostly in the release of Large Language Models (LLMs), which can produce advanced outputs but also require careful crafting of meaningful instructions to achieve desired results. Prompt engineering is a crucial step in talking with LLMs, involving understanding the model's capabilities and limitations, crafting clear and concise prompts, and iteratively testing and refining them based on the model's responses. Various techniques such as zero-shot prompting, one-shot prompting, few-shot prompting, chain-of-thought prompts, contextual augmentation, meta-prompts, prompt combinations, and human-in-the-loop can be employed to tap into an endless array of possibilities in generating creative output or producing accurate results. Effective prompt engineering requires a blend of technical and soft skills including communication, collaboration, creative thinking, basic AI + NLP knowledge, writing styles, programming knowledge, and data analysis, enabling users to craft prompts that enhance the performance and relevance of AI responses, leading to more intuitive and efficient human-AI collaborations.