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LIVE WEBINAR WITH DEMO

How to evaluate LLM accuracy for domain-specific use cases

July 18, 2024

10:00 AM PT / 1:00 PM ET

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Rebekah Westerlind

Software Engineer
Snorkel AI

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Vincent Chen

Product Director
Snorkel AI

Register now

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LLM evaluation is critical for generative AI in the enterprise, but measuring how well an LLM answers questions or performs tasks is difficult. Thus, LLM evaluations must go beyond standard measures of “correctness” to include a more nuanced and granular view of quality.

In practice, enterprise LLM evaluations (e.g., OSS benchmarks) often come up short because they’re slow, expensive, subjective, and incomplete. They leave AI initiatives blocked because there is no clear path to production quality.

In this webinar, Vincent Sunn Chen, Founding Engineer at Snorkel AI, and Rebekah Westerlind, Software Engineer at Snorkel AI, will discuss the importance of LLM evaluation, highlight common challenges and approaches, and explain the core concepts behind Snorkel AI's approach to data-centric LLM evaluation.

Join us to learn more about:
  • Understanding the nuances of LLM evaluation
  • Evaluating LLM response accuracy at scale
  • Identifying where additional LLM fine-tuning is needed
  • Date: July 18, 2024
    Time: 10:00 AM PT | 1:00 PM ET

Schedule

Tuesday, March 12, 2024

7:45 PM to 8:30 PM

Arrive and mingle

8:30 PM to 10:45 PM

Dinner and conversation with data science leaders

Speakers

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Rebekah Westerlind

Software Engineer
Snorkel AI

Rebekah Westerlind is a full-stack software engineer at Snorkel AI on the product engineering team. She graduated from Cornell University in 2022 with degrees in Computer Science and Operations Research & Information Engineering. Driven by a desire to always be learning, Rebekah loves jumping in on new projects and surrounding herself with experts.

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Vincent Chen

Product Director
Snorkel AI

Vincent is a Product Leader at Snorkel AI, where he leads AI data development workflows in Snorkel Flow. He joined Snorkel as a Founding Engineer and grew the ML Engineering team from zero-to-one. Before Snorkel, he was a graduate student at the Stanford AI Lab and led research on the foundations of data-centric ML systems.