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Golden Datasets have long been a reliable method for measuring AI prompt performance. But as AI innovation moves fast, companies need a more agile, flexible, and cost-effective solution to stay ahead of their competition. Enter random sampling of AI prompt performance—a cutting-edge approach that adapts to real-world data and drives scalable performance for QA Wolf customers. In this on-demand webinar, Sr. Director of AI Nishant Shukla and Helicone co-founder & CEO Justin Torre cover: 🔹 The benefits and limitations of Golden Datasets 🔹 How random sampling streamlines AI development and enhances performance 🔹 QA Wolf's innovative use of Helicone's tools to optimize AI testing Learn more: https://www.qawolf.com/ai --- 0:00 - Welcome and introductions 01:19 - The challenges of evaluating prompts for LLMs 05:33 - QA Wolf's unique hurdles for prompt evaluation 07:15 - Golden datasets: The old conventional wisdom for prompt evaluation 09:48 - Why QA Wolf avoids golden datasets 15:57 - Random sampling: An innovative approach to prompt evaluation 19:08 - Closing the gap between evaluation data and real-world data with random sampling 21:40 - Demonstrating Helicone's random sampling at QA Wolf 26:29 - Impact of random sampling at QA Wolf 30:19 - Using better AI prompt evaluations to deliver higher quality coverage faster