Jay discusses the evolving landscape of prompting and fine tuning in large language models. He highlights the significant advancements in token limits, allowing for greater data ingestion through prompting alone. While fine tuning has its place, it can be resource-intensive, making it crucial to evaluate when to implement it effectively. An example illustrates how a startup successfully combines both techniques to translate complex project descriptions into a specific workflow language.
