Most AI MVPs do not fail because the model is not smart enough. They fail because the system arou...Most AI MVPs do not fail because the model is not smart enough. They fail because the system arou...
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Most AI MVPs do not fail because the model is not smart enough.
They fail because the system around it is poorly designed.
Teams often start by choosing between GPT, Claude, or Gemini. But the real questions are: - How is the AI output validated? - What happens when it is wrong? - Can users review and correct the result? - How do you track quality over time?
In production AI products, the model is only one part.
The real value comes from business logic, data pipelines, validation, human approval, monitoring, and fallbacks.
Start with the workflow and business outcome.
Choose the model after that.
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