How do you prepare a dataset to fine-tune an LLM, and why does data quality dominate?
Fine-tuning lives or dies on data, not on hyperparameters. What shows depth is spelling out what makes a set good (quality, diversity, format, dedup) and defending why a few thousand clean examples outperform a million noisy ones.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Fine-tuning lives or dies on data, not on hyperparameters. What shows depth is spelling out what makes a set good (quality, diversity, format, dedup) and defending why a few thousand clean examples outperform a million noisy ones.
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.
