Software development today extends beyond choosing programming languages and frameworks, especially as AI-assisted tools become part of everyday workflows. While these tools can improve productivity, real-world projects still face familiar challenges such as changing requirements, uncertain timelines, and incomplete information. In this environment, flexible system design and iterative planning remain essential, regardless of how advanced the tooling becomes.
Another area where experience matters is communication and decision-making in AI-influenced projects. The introduction of AI can add complexity around expectations, data quality, and responsibility for outcomes. Clear documentation, early feedback loops, and simple, understandable system architectures often help teams use AI effectively without increasing long-term maintenance or technical debt.
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