Have you ever onboarded a new teammate and watched every email, every call, every chat—just to make sure they’re getting it right? 🤔

That’s exactly how we need to treat our AI. When you bring someone new onto your support team, you invest in training, shadowing, feedback loops… all until you trust them to own your customer experience. So why wouldn’t you do the same with AI?

Here’s the twist: AI doesn’t ask for coffee breaks, but it can still go off-script—hallucinate answers, miss a detail, or trip over a nuance. That’s where evaluation comes in. 🚀

Imagine an automated referee that scores every AI response: “✅ Accurate,” “❌ Needs improvement,” “⚠️ Potential hallucination.” Suddenly, you’ve surfaced every question the AI stumbled on, and your team knows exactly where to focus their energy—refining knowledge bases, tweaking prompts, or bolstering documentation.

In a world where “answer quality” is the ultimate KPI, evaluation isn’t just a nice-to-have—it’s the steering wheel that keeps AI on course.

Next time you launch an AI playbook, don’t skip this step. Build in evaluation from day one, and watch your AI go from “helpful” to “exceptional.” 🌟

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