Why AI apps fail without LLM performance testing
It’s now a familiar story: a company launches a new AI chatbot or AI-powered search feature. It works great in the demo. Then real customers show up, all at once, all asking questions at the same time and the feature that impressed everyone in the boardroom starts crawling, timing out, or giving up entirely. It’s a clear sign that LLM performance testing never happened before launch.
The core problem is standard performance testing doesn’t capture what matters for LLM-powered features. This article breaks down why that gap exists and how to close it before launch.
This gap is happening across industries. According to McKinsey’s 2025 State of AI report, the share of organizations using generative AI in at least one business function jumped from 33% to 72% in a single year. Features powered by an LLM (large language model) are moving from optional extras to core parts of the...
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