In this paper, we propose an efficient knowledge-based automatic model generation (KAMG) technique aimed at generating microwave neural models of the highest possible accuracy using the fewest accurate data. The technique is comprehensively derived to integrate three distinct powerful concepts, namely, automatic model generation, knowledge neural networks, and space mapping. For the first time, we simultaneously utilize two types of data generators, namely, coarse data generators that are approximate and fast (e.g., two-and-one-half-dimensional electromagnetic), and fine data generators that are accurate and slow (e.g., three-dimensional electromagnetic). Motivated by the space-mapping concept, the KAMG technique utilizes extensive coarse data, but fewest fine data to generate neural models that accurately match the fine data. Our formulation exploits a variety of knowledge neural-network architectures to facilitate reinforced neural-network learning from coarse and fine data. During neural model generation by KAMG, both coarse and fine data generators are automatically driven using adaptive sampling. The KAMG technique helps to increase the efficiency of neural model development by taking advantage of a microwave reality, i.e., availability of multiple sources of training data for most high-frequency components. The advantages of the proposed KAMG technique are demonstrated through practical microwave examples of MOSFET and embedded passive components used in multilayer printed circuit boards.