Prompting Dynamics
The conversation delves into the nuances of prompting in machine learning, likening it to tea leaf reading due to its unpredictable nature. Sara emphasizes the challenge of understanding high dimensional spaces and the potential for general prompts across different architectures. Tim counters with the idea that prompts hold intrinsic value, suggesting they may evolve but will always retain a degree of unpredictability.In this clip
From this podcast
Machine Learning Street Talk (MLST)
#92 - SARA HOOKER - Fairness, Interpretability, Language Models
Related Questions
Is there anyone taking a different approach to prompt engineering for large language models that makes the process more accessible to a wider audience?
Is there anyone taking a different approach to prompt engineering for large language models that makes the process more accessible to a wider audience, as discussed in the episode Holistic Evaluation of Generative AI Systems // Jineet Doshi // #280 and the clip LLMs as Jury, as well as in the episode Collaboration & evaluation for LLM apps and the clip Fine Tuning Insights?
Is there anyone taking a different approach to prompt engineering for large language models that makes the process more accessible to a wider audience, as discussed in the episode Treating Prompt Engineering More Like Code // Maxime Beauchemin // MLOps Podcast #167 and the clip Solving Text to SQL Challenges?