This paper introduces two novel machine learning based approaches to improvehadron-level simulation by integrating experimental observables: MicroscopicAlterations Generated from IR Collections (MAGIC), which fine-tunes normaliz-ing flows, pre-trained on simulated data from P YTHIA , on experimental observables,and the Collective Reweighting Method (CRM), which reweights existing fragmen-tation functions to match experimental observables with a two-step procedure thatmakes use of a observable-level classifier and hadron-level particle cloud-basedregressor. Both methods show a promising direction towards data-driven modelsfor hadronization.