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Golden datasets were the gold standard for testing AI prompts until fast-changing production data made static tests rigid, costly, and stale. QA Wolf takes a different approach: random sampling against live data to keep prompt evaluations accurate and relevant as tasks shift daily.

Nishant Shukla, QA Wolf’s Senior Director of AI, and Justin Torre, CEO & Co-founder of Helicone, join host Caleb Masters to break down how real-time sampling closes the gap between lab results and real-world performance.

In this webinar, you’ll learn about:

Why golden datasets fall short for evolving LLM and agent workflows.
How random sampling keeps prompt evaluations true to production data through a Helicone demo.
The benefits of reducing overfitting to speed up improvements.

Watch how QA Wolf uses live sampling to keep tests accurate — and how you can adapt the same ideas for your own LLM stack.