Large language models, such as AI21 Labs’ Jurassic-1 and OpenAI’s GPT-3, exhibit unprecedented versatility in performing many different language tasks.
Using prompt engineering to adapt the model’s behavior to their specific needs, developers and even non-coders can build sophisticated AI applications from scratch in a matter of days. In this talk, we review the technology powering large language models and discuss their capabilities and limitations.
A significant challenge that developers relying on large language models face is scaling up their usage economically without sacrificing quality. We will present approaches to overcome the cost-quality tradeoff in the context of a product life cycle, by starting with rapid prototyping using prompt engineering and large language models, and then evolving to training and deploying a more affordable custom model, optimized to perform a specific task at scale.