The article discusses seven advanced prompt engineering techniques for optimizing large language models (LLMs) like ChatGPT and Gemini. Techniques include meta prompting, least-to-most prompting, multi-task prompting, role prompting, task-specific prompting, program-aided language models (PAL), and chain-of-verification (CoVe). Each method aims to enhance the performance and accuracy of LLMs by structuring prompts effectively and addressing complex tasks, ultimately improving the quality of the generated output.