Prompt-Driven Efficiencies in LLMs

Generative AI Webinar Series

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It’s no secret that Large Language Models (LLMs) come with many challenges. Through prompt economization and in-context learning, we can address two significant challenges: model hallucinations and high compute costs.

We will explore creative strategies for optimizing the quality and compute efficiency of LLM applications. These strategies not only make LLM applications more cost-effective, but they also lead to improved accuracy and user experiences. We will discuss the following techniques:

  • Prompt economization
  • Prompt engineering
  • In-context learning
  • Retrieval augmented generation

Join us to learn about these smart and easy ways to make your LLM applications more efficient.

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Speakers



Eduardo Alvarez

Senior AI Solutions Engineer at Intel

Hosts



Sancha Huang Norris

Generative AI Marketing Lead at Intel's Data Center and AI Business Unit