The need for detailed molecular information from kinetic models has given rise to the practice of modeling the chemistry at either the molecular or mechanistic level. These models are often used to predict the product spectrum of complex process chemistries involving complex feedstocks and, hence, they are extremely large and often consume prohibitively large CPU times. These models therefore need to be tailored to emphasize mainly the important chemistry only. To this end, we have developed a model reduction technique involving the "seeding" of key intermediates and molecules along the important reaction paths in the complex chemistry. This technique directs the model growth toward the most important and experimentally observed products at the expense of the unimportant part of the reaction network. The technique is illustrated using the acid cracking chemistry for n-heptane as an illustrative example. Comparison of a reduced model and a full model reveals considerable time and size savings without loss of accuracy.