How to evaluate LLM accuracy for domain-specific use cases

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, 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.

Watch this on demand webinar to learn more about:

• The nuances of LLM evaluation
• How to evaluate LLM response accuracy at scale
• Identifying where additional LLM fine-tuning is needed