Originally published on AI Tech Connect.
What you need to know Synthetic data has become one of the most powerful and most misused tools in the fine-tuning toolkit. Used well, it lets a small team in Bengaluru or Bristol bootstrap a training set for a niche domain in an afternoon, widening coverage around a handful of real examples and unlocking a model that would otherwise need months of human annotation. Used carelessly, it does something subtler and more dangerous: it produces a dataset that looks plausible, passes a casual eyeball check, and quietly drags your model toward the bland, low-variance distribution that recent research calls model collapse. The gap between those two outcomes is not luck. It is a pipeline. This guide builds that pipeline end to end. It covers when synthetic data is genuinely the right move and when…
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