Scalable Synthetic Data Generation with LLMs
Human data annotation is expensive, and projections suggest the stock of high-quality public text could be exhausted within this decade (Villalobos et al., 2024). Synthetic data lets you precisely target capability gaps. The question is not whether to use synthetic data, but how to do it without collapsing into repetitive, low-diversity slop. Here is a survey of the key paradigms and the best open-source tooling to put them into practice.