What is data augmentation, and how does it differ across modalities (images, text, audio)?
Data augmentation cheaply expands training data and regularizes models, but valid transforms differ by modality. What matters is the label-preserving constraint and why text is the hard one. Here is the answer.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Data augmentation cheaply expands training data and regularizes models, but valid transforms differ by modality. What matters is the label-preserving constraint and why text is the hard one. Here is the answer.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
