why you suck at prompt engineering (and how to fix it)
AI Summary
In this video, the speaker discusses the common issues people face in prompt engineering, particularly those stuck in the midwit range. He illustrates the difference between basic and advanced techniques in prompt engineering, emphasizing the importance of understanding the science behind language model (LLM) prompting. The video aims to help viewers improve their skills and move from basic conversational prompting to mastering single-shot prompting, enabling them to build effective AI systems. Key components discussed include:
- Understanding Prompt Engineering:
- Distinction between low- and high-level prompting.
- Importance of not relying solely on prompt templates.
- Conversational vs. Single-shot Prompting:
- Conversational prompting is forgiving but limited to personal use.
- Single-shot prompting allows for scalable AI tasks without human intervention.
- Techniques for Effective Prompting:
- Role Prompting: Assign advantageous roles to improve output accuracy by up to 25%.
- Chain of Thought Prompting: Encourages step-by-step processing, increasing accuracy significantly on complex tasks.
- Emotional Prompts: Using emotionally charged language enhances responsiveness, especially on complex tasks.
- Examples (Few-shot Prompting): Providing input-output pairs to guide models leads to better accuracy.
- Markdown Formatting: Using structure in prompts improves both formatting and understanding for the model.
- Practical Examples and Case Studies:
- The creation of an email classification system is highlighted as a practical application for the discussed techniques.
- Conclusion:
- The speaker stresses that mastering these techniques is critical for success in the AI space and to generate value for clients. The importance of being skilled in prompt engineering is reiterated, as it impacts future advancements in AI business applications.
Overall, the video provides a comprehensive guide for improving prompting skills to achieve better results from AI models, emphasizing that effective prompt engineering is crucial for leveraging AI technologies.