The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Mental Models for Advanced ChatGPT Prompting with Riley Goodside - #652
Oct 23, 2023
Riley Goodside, a staff prompt engineer at Scale AI, shares insights on mastering prompt engineering for large language models. He dives into the limitations and capabilities of LLMs, emphasizing the intricacies of autoregressive inference. Goodside discusses the effectiveness of zero-shot vs. k-shot prompting and the crucial role of Reinforcement Learning from Human Feedback. He highlights how effective prompting acts as a scaffolding structure to achieve desired AI responses, blending technical skill with strategic thinking.
39:58
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Quick takeaways
- Prompt engineering involves restructuring problems into checklists or decision trees to guide the model's response, improving the output quality.
- Crafting well-designed case shot prompts with artificial rare classes or specific edge cases helps the model learn how to handle scenarios and generate appropriate responses.
Deep dives
Prompt Engineering and Scaffolding
Prompt engineering involves restructuring problems into checklists or decision trees to guide the model's response. This helps avoid known limitations and improves the quality of the output. Using scaffolding, such as providing context and structuring prompts, is more valuable than linguistic cleverness.
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