The rush to implement AI can’t be denied, but without proper testing and human validation, these systems can quickly lose their value and relevance.

The rush to keep up with the Joneses when it comes to implementing AI is undeniable. As more AI startups and larger organizations hurry to integrate AI into their tech stack, there’s one critical step that often gets overlooked: validating AI usage in applications against real human output. This is key to making sure the AI produces or evaluates content in a way that aligns with what people actually expect.

This is, after all, what makes AI so valuable to humans in the first place, and it’s why companies like OpenAI and Anthropic invest significant resources in having real humans calibrate and measure AI output. And the results speak for themselves: today’s AIs are incredibly useful and helpful in everyday life across countless applications.

The problem happens when companies start adding prompts or making other tweaks to the AI, unintentionally changing how the application behaves in unpredictable ways once it’s out in the wild. The problem gets even worse when a product’s prompts are continually tweaked without retesting, leading to more unpredictable outcomes. If prompts and AI configuration changes aren’t constantly tested against real human standards, the AI can quickly lose touch with what people need or expect.

AI use cases, as magical as they may seem, still need good old-fashioned testing—think test suites, A/B testing, and even edge case testing—against data that’s been vetted by real humans. It’s the only way to make sure the model stays relevant in the real world.