From AI Prototype to Production: Why Product Thinking Matters
A working AI demo is not a reliable product. Learn the steps for evaluation, security, UX and operations.
The demo gap
A prototype may succeed on a few prompts and still fail on real inputs, access permissions, latency or cost. Product thinking starts with a measurable task and designs for what happens when the model is uncertain or wrong.
Five production checkpoints
Define the user outcome; build a representative evaluation set; enforce least-privilege data and tool access; provide fallback and human handoff; monitor quality, response time and spending. These checks make iteration safer and more predictable.
Use the simplest architecture that works
Not every workflow needs an autonomous agent. A deterministic API integration or a retrieval-based assistant can be easier to validate. Add agent autonomy only when a real use case and evaluations justify it.
Let's ship your AI feature
I can help scope and implement AI chats, assistants, agent workflows and LLM integrations with product UX and maintainability in mind. Describe your current stage in the contact form.
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