why you suck at prompt engineering (and how to fix it)

In this video, the speaker discusses prompt engineering, comparing it with a meme about 'midwit' behavior, where people in the middle range overcomplicate simple processes unlike those with low or high IQ who reach the same simple solutions. The aim is to move viewers from being 'midwits' who rely on generic prompt templates to becoming insightful prompt engineers.

The speaker highlights how understanding the science and principles behind prompt engineering can help one become a proficient AI system user. This expertise allows for extracting maximum value from language models (LLMs), making AI systems more efficient and useful.

The speaker introduces the concept of conversational prompt engineering versus single shot prompting. Conversational prompting involves interactive prompts with follow-up questions, typically used for personal productivity. Single-shot prompting, however, involves creating reliable, automated systems with no further human intervention.

The speaker emphasizes the importance of mastering single-shot prompt engineering to build scalable AI systems that can execute tasks reliably, highlighting its potential to generate substantial economic value. The necessity of English proficiency in prompt creation is explained, citing it as the new 'programming language'.

The speaker asserts that mastering prompt engineering is crucial for working with AI voice systems, AI agents, AI task automations, and building custom AI tools. Such skills ensure the creation of valuable, efficient systems. They contrast effective prompt engineers, who optimize prompts to use cheaper AI models, with those who rely on more expensive models due to inefficient prompts.

A 'prompt formula' is introduced, consisting of role, task, specifics, context, examples, and notes. These components are based on scientific findings that improve prompt performance, such as role-specific prompting, chain-of-thought prompting, and emotional stimuli. The goal is to raise a user's prompt creation ability, enhancing the effectiveness and efficiency of AI systems.

Detailed techniques to improve each aspect of the prompt formula are discussed. Role prompting can enhance accuracy by 10-25%. Task-specific instructions benefit from chain-of-thought prompting, providing a 10-90% accuracy boost depending on task complexity. Emotional stimuli improve complex task accuracy by 115%, showing the importance of context in prompts.

The importance of providing examples is emphasized, with few-shot prompts significantly increasing prompt accuracy. Giving strategic examples helps the model understand desired input-output relationships. Notes are also critical for reiterating key issues and structuring prompts optimally, leveraging findings like the 'lost in the middle' effect to enhance performance.

Incorporating markdown for structured and readable prompts is advised, as format and structure contribute significantly to better AI response. While no definitive research is cited, evidence from OpenAI practices suggests structured inputs improve AI performance. Practical benefits of markdown include better maintenance and readability.

The speaker highlights real-world applications using AI systems developed beyond simple chat prompts, encouraging transition from GPT-4 to cheaper models like GPT-3.5 as effective prompting can replicate high-quality outputs of more expensive models, emphasizing economic benefits without sacrificing performance.