video1.0<iframe src="https://www.loom.com/embed/ab25eee1980749dea355bff3f8b50d82" frameborder="0" width="1664" height="1248" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>12481664Loomhttps://www.loom.com12481664https://cdn.loom.com/sessions/thumbnails/ab25eee1980749dea355bff3f8b50d82-99b1c87e4477e5d3.gif1009.7357We explain why traditional testing methods fail for LLMs: unlike deterministic software systems, LLMs are probabilistic and unpredictable, making standard testing inadequate. Many companies deploy AI with minimal testing, leading to poor user experiences when edge cases (affecting 5% of queries) impact thousands of users. The solution is creating custom benchmarks tailored to your specific use case and users, rather than relying on generic academic benchmarks that don't reflect real-world scenarios. The recommended approach involves using simple Google Sheets with query-answer pairs, LLM outputs, and human evaluation ratings. Human evaluation remains crucial and warns against chasing higher benchmark numbers beyond 85% accuracy, as gains rarely translate to better user satisfaction. Success depends on understanding your users and focusing on practical utility rather than abstract metrics. Both quantitative testing (for strict outputs like math) and qualitative testing (for subjective quality) are important, but custom benchmarks specific to your domain will always outperform generic ones.