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Applied AIBuild note6 min read2026-02-12

What I Learned Building an AI Conversational Agent

The hard part was not making the assistant speak. The hard part was teaching the product where to slow down, show its sources, and refuse false confidence.

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A working chat box is not the product

The first version of an AI assistant can feel impressive very quickly. A user types a question, the system responds, and the demo looks alive. TrustMed AI pushed me to look past that first impression.

For health-adjacent information, the product is not the chat box. The product is the full decision system around the chat box: what context is retrieved, what sources are visible, how uncertainty is expressed, and when the assistant should stop trying to be helpful.

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Guardrails are product design

I started thinking of guardrails less as a technical wrapper and more as product behavior. The assistant needs a personality, but it also needs boundaries users can feel.

That means answer templates, escalation language, citation placement, and UI states all matter. Safety is not one middleware function. It is distributed across the full experience.

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The next iteration

The strongest next step is evaluation. The assistant needs scenario tests, source-quality checks, and clearer feedback loops before it can become more than a polished prototype.

The project taught me that responsible AI work is equal parts engineering, writing, product judgment, and humility.