Prompt-Driven Efficiencies in LLMs

Register now

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.

First Name

Please enter a first name.

First name must be at least 2 characters long.

First name must be less than 250 characters long.

Please enter a first name.

Last Name

Please enter a last name.

Last name must be at least 2 characters long.

Last name must be less than 250 characters long.

Please enter a last name.

Business Email Address

Please enter an email address.

Please enter a valid email address.

Email Address must be less than 250 characters.

Country/Region

Please select a country/region.

Your registration cannot proceed. The materials on this site are subject to U.S. and other applicable export control laws and are not accessible from all locations.

Profession

Please select a profession.

Company

Please enter a company name.

Company name must be at least 2 characters long.

Company name must be less than 250 characters long.

Please enter a company name.

Intel strives to provide you with a great, personalized experience, and your data helps us to accomplish this.

I consent to Intel collecting and using my personal information as described below.

Above consent required for submission.

I expressly consent to Intel transferring my Personal Data outside of China to Intel U.S. headquarters as described in the Intel Privacy Notice Supplement for Users in China.

Above consent required for submission.

Intel may contact you for marketing-related communications. You can opt out at any time.

By submitting this form, you are confirming you are age 18 years or older. Intel may contact you for marketing-related communications. You can opt out at any time. To learn more about Intel's practices, including how to manage your preferences and settings, visit Intel's Privacy Notice.

By submitting this form, you are confirming you are age 18 years or older. Intel will process your Personal Data for the purpose of this business request. To learn more about Intel's practices, including how to manage your preferences and settings, visit Intel's Privacy Notice.

Prompt-Driven Efficiencies in LLMs

Register now

Speakers

Eduardo Alvarez

Hosts

Sancha Huang Norris