How to Scope an AI Engineering Project You Can Actually Complete

Many AI student projects fail not because of model complexity but due to poorly defined scope, according to a tutorial published on DEV Community. The guide recommends framing every project around a clear input, a measurable output, an identified user, and a single core technical question. It advises starting with simple baseline models — such as logistic regression or random forests — before attempting complex neural networks, to establish a meaningful performance benchmark. The tutorial also warns against data leakage, particularly in engineering datasets where a random train-test split can expose the model to near-identical readings from the same machine. Choosing the right evaluation metric — precision, recall, or F1 score — is highlighted as critical, especially when dealing with imbalanced datasets where raw accuracy can be misleading.