Microstructural and mechanical properties of steels are significantly influenced by the concomitant process of austenite grain growth and precipitation. Modeling these phenomena – essential for material design and quality predictions – largely relies on statistical data and comprehensive verification of modeling approaches. Thus, the present thesis introduces an entirely new approach for the combined validation of austenite grain growth and precipitation models utilizing a High-Temperature Laser Scanning Confocal Microscope (HT-LSCM). The new approach is based on in-situ mapping of the austenite grain size evolution. As a first task, the HT-LSCM is tested regarding its potentials and limitations for time-resolved grain growth measurements. The method is proven to be a fast and flexible approach, enabling recording an entire grain growth evolution curve in just one experiment. Several influencing parameters, mainly concerning the impact of the free surface, are discussed, and a set of criteria for operation formulated. Subsequently, the new validation procedure is introduced and tested on a matrix of low-carbon steels featuring a precipitation-free standard and several alloys of the same base composition but different AlN fractions. The interaction of migrating grain boundaries and precipitations is proven to allow a qualitative judgment over precipitation evolution and thermal stabilities. Most significant grain growth inhibition is found for the highest N-values due to the largest volume fractions of AlN. Additionally, dissolution temperatures of AlN are analyzed using both evolution curves and grain size statistics. A grain growth model for the precipitation-free base alloy is defined, including the effect of a solute drag. Based on the HT-LSCM measurements, input parameters for a precipitation model are specified in an iterative process, and pinning forces are calculated. The simulated evolution of precipitate size and phase fraction is validated by incorporating the Zener pinning into the previously defined grain growth model. Grain growth modeling predictions and HT-LSCM measurement data show an excellent agreement for all alloys and thermal regimes. Thus, the grain growth model state parameters and precipitation simulation input parameters are verified. The proposed method is a valuable tool for defining and validating modeling approaches and improving through-process modeling. Further, it can significantly increase the fundamental understanding of migrating grain boundaries interacting with second-phase particles.