Mental Models for Advanced ChatGPT Prompting with Riley Goodside - #652
Analysis
This article from Practical AI discusses advanced prompt engineering techniques for large language models (LLMs) with Riley Goodside, a staff prompt engineer at Scale AI. The conversation covers LLM capabilities and limitations, the importance of mental models in prompting, and the mechanics of autoregressive inference. It also explores k-shot vs. zero-shot prompting and the impact of Reinforcement Learning from Human Feedback (RLHF). The core idea is that prompting acts as a scaffolding to guide the model's behavior, emphasizing the context provided rather than just the writing style.
Key Takeaways
- •The article highlights the importance of understanding LLM behavior for effective prompting.
- •It emphasizes the difference between k-shot and zero-shot prompting strategies.
- •Prompting is presented as a method to shape model output by leveraging context.
“Prompting is a scaffolding structure that leverages the model context, resulting in achieving the desired model behavior and response rather than focusing solely on writing ability.”