A strong graduation project starts with a problem that is specific enough to validate and broad enough to demonstrate engineering value.
Before implementation, define measurable success criteria, constraints, interfaces and risks. This prevents teams from confusing activity with progress.
Build a baseline early. For AI projects that may be a simple classical model; for embedded systems it may be a sensor-to-dashboard proof of concept. Baselines make later improvements measurable.
Documentation should evolve with the system. Keep decisions, test cases, diagrams and results synchronized so the final report reflects the actual engineering process.
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