Prompt optimization is a critical component of improving Large Language Model (LLM) performance. Different techniques, including few-shot prompting, meta-prompting, and gradient-based tuning, offer systematic ways to enhance prompts at scale. Automating this process through frameworks like DSPy enables scalable and data-driven improvements, reducing the reliance on manual prompt engineering. Effective prompt optimization requires structured experimentation and continuous iteration, and tools such as Arize Phoenix facilitate seamless versioning of prompts and easy comparison of different strategies. By leveraging these techniques and tools, practitioners can efficiently refine their LLMs to achieve better accuracy, efficiency, and consistency in their outputs.