Cameron Wolfe, PhD
Director of AI | Rebuy Engine
Cameron earned his Ph.D. in Computer Science from Rice University (advised by Dr. Anastasios Kyrillidis) in Houston, TX. His research interests are related to math and machine/deep learning, including non-convex optimization, theoretically-grounded algorithms for deep learning, continual learning, and practical tricks for building better systems with neural networks. Cameron is currently the Director of AI at Rebuy, a personalized search and recommendations platform for D2C e-commerce brands. He works with an amazing team of engineers and researchers to investigate topics such as language model agent systems, personalized product ranking, search relevance, and more.
All Sessions by Cameron Wolfe, PhD
Prompt Engineering: From Few Shot to Chain of ThoughtLLMs | Intermediate
The popularization of large language models (LLMs) has completely shifted how we solve problems as humans. In prior years, solving any task (e.g., reformatting a document or classifying a sentence) with a computer would require a program (i.e., a set of commands precisely written according to some programming language) to be created. With LLMs, solving such problems requires no more than a textual prompt. In this session, we will provide a basic primer on the topic of prompt engineering, as well as cover examples of notable prompt engineering techniques ranging from basic strategies like few-shot learning to more advanced approaches like chain of thought prompting.