ChatGPT Prompt Engineering Techniques

In this video, Patrick Storm guides viewers through 10 levels of prompt engineering techniques, from basic to advanced, to maximize the effectiveness of ChatGPT and other large language models (LLMs). Starting with basic requests, Patrick demonstrates how simple formatting and politeness can enhance responses. He then moves to more advanced techniques like focused requests, giving examples, and self-reflection. The video introduces system prompts, which provide context and instructions to guide ChatGPT’s responses, and the use of personas to improve accuracy. Patrick explains the Chain of Thought (CoT) technique, which involves prompting ChatGPT to explain its reasoning step-by-step. He also highlights the effectiveness of self-prompting, where ChatGPT generates its own prompts to achieve better results. The final and most advanced technique is the CO-STAR framework, a structured approach to crafting prompts that includes context, objective, style, tone, audience, and response format. The video concludes with an example of using CO-STAR to create a more effective Facebook post. Patrick encourages viewers to experiment with these techniques to enhance their interactions with AI and improve the quality of responses.