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How do you prepare a dataset to fine-tune an LLM, and why does data quality dominate?

Fine-tuning is mostly a data problem, not a hyperparameter one. The signal is naming what makes a good set (quality, diversity, format, dedup) and arguing why a few thousand clean examples beat a million noisy ones.

Updated Aug 2026 · Grounded in real Applied AI Engineer interview loops and written to a senior-engineer editorial bar.

Fine-tuning is mostly a data problem, not a hyperparameter one. The signal is naming what makes a good set (quality, diversity, format, dedup) and arguing why a few thousand clean examples beat a million noisy ones.

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Next in this trackHow and when do you use synthetic data (LLM-generated) for training or fine-tuning?Next in this trackWhat is G-Eval / LLM-as-a-judge, and how do you make it reliable?Next in this trackOffline vs online evaluation: what each measures and why you need both.
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