LLMs are powerful, but how do you verify that the output is accurate—especially when dealing with data-heavy content like sports scores, financials, or live updates?

This talk focuses on practical evaluation techniques for LLM-generated content:
- Incorporating Human-in-the-Loop evaluation workflows.
- Building custom, automated evaluation pipelines tailored to your use case.
- Using prompt engineering and iterative workflows to fine-tune accuracy.
- Case study: How we designed a verifiable, fact-checked content pipeline for sports data, ensuring correctness at scale.

Learn how to create LLM applications you can trust in real-world, high-stakes environments.