Webinar: Prompt-Driven Efficiencies in LLMs
Optimize outputs through in-context learning, retrieval augmented generation, and prompt economization.
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Prompt-Driven Efficiencies in LLMs
Wednesday, January 24, 2024 10:00am – 11:00am PT
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Unlock the Potential of Prompt-driven Efficiencies in LLMs
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 on Wednesday, January 24, 2024 10:00am – 11:00am PT to learn about these smart and easy ways to make your LLM applications more efficient.
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Eduardo Alvarez
Senior AI Solutions Engineer at Intel, specializing in architecting AI/ML solutions, MLOps, and deep learning.
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Sancha Huang Norris (moderator)
Generative AI Marketing Lead at Intel's Data Center and AI Business Unit
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Our Generative AI series is just getting started. Stay tuned for more details on our 2024 webinar calendar.
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