The HabEx and LUVOIR mission concepts reported science yields for mission scenarios in which the instruments must search for potentially habitable planets, determine their orbits, and, if worthwhile, invest the integration time for a spectral characterization. The common comparison of yield performed by the Exoplanet Exploration Program Office’s Standard Definitions and Evaluation Team (ExSDET) used two yield codes, AYO and EXOSIMS, that had very different approaches yet achieved good consistency with identical astrophysical inputs and instrument parametric descriptions. The ExSDET results established a baseline yield for these mission architectures when conducting a blind search. How can the yield be improved by prior knowledge, such as improved ground-based surveys? If perfect prior knowledge were possible, what is the yield limit when faced with realistic mission constraints, particularly solar avoidance angles, fuel consumption, and observation scheduling? Using EXOSIMS, we evaluate the impact of prior knowledge on yield for starshade only, coronagraph only, and hybrid architectures. We use perfect prior knowledge to establish an upper bound on yield and use partial prior knowledge from a potential future Extreme Precision Radial Velocity instrument with 3 cm/s sensitivity. We detail a modeling framework that performs dynamically responsive observation scheduling with realistic mission constraints. We evaluate exo-earth yields against three metrics of spectral characterization for four mission architectures and these three levels of prior knowledge. The EPRV provided prior knowledge increases yields by ~30% and accelerates by a factor of 3 - 6 the time to achieve half of the yield of the mission. Prior knowledge makes all the mission architectures more nimble and powerful, most especially starshade-based architectures. With prior knowledge, a small telescope with a starshade can achieve comparable yield to a larger telescope with a coronagraph.