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How do you decide whether a problem actually needs AI/ML, or whether traditional software is better?

Strong applied-AI engineers are the ones who refuse to reach for ML when they shouldn't. The signal is judgment: ML earns its complexity only under specific conditions, and otherwise rules and heuristics win.

Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.

Strong applied-AI engineers are the ones who refuse to reach for ML when they shouldn't. The signal is judgment: ML earns its complexity only under specific conditions, and otherwise rules and heuristics win.

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