Traditional software lets us see problems directly in code.
AI engineering is different.
With LLMs, issues hide in prompts and behavior, and the system itself is uncertain.
Change one part, and the impact on others is often unpredictable.
This challenges engineers trained in classic architectures, where fixes were local and controllable.
AI should not be treated like alchemy. No wishing. No blind trial and error.
If we want reliable AI systems, we must apply logic, discipline, and scientific thinking. Clear hypotheses. Careful experiments. Rigorous evaluation.
AI changes software engineering, but real engineering still matters.
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