When LLM Demos Fail: Four Hard Questions in AI Product Engineering
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A recent backlash against a newly released LLM highlighted a common industry pitfall: prioritizing benchmark scores over engineering execution. This article examines four systemic challenges developers face when moving AI products from demo to production, covering the gap between native reasoning and tool integration, coding benchmark discrepancies, hallucination evaluation metrics, hidden infrastructure costs, and compliance risks in global markets.
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