Prompt-Driven Efficiencies for LLMs (with pre-trained model) | Intel Business

Опубликовано 27 февраля 2025, 14:29

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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Prompt-Driven Efficiencies for LLMs (with pre-trained model) | Intel Business
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