Failures in clusters running large-scale AI workloads can result in decreased utilization. Because the cost of a failure in such AI workloads is high (as it requires restarting the entire job from a previous checkpoint), there are many mechanisms in place to ensure that the failures are mitigated, and the impact of a failure is minimized. However, these mechanisms have many knobs and parameters, all of which must be carefully tuned based on the system and cluster's characteristics. We built AIReSim, a discrete event simulator to evaluate the different design choices during the failure, recovery, scheduling and repair processes for a cluster running a large-scale AI workload. AIReSim allows the system designer to systematically evaluate the effects of the different knobs and parameters on the overall end-to-end reliability of the system. Further, AIReSim can be used to identify which knobs or parameters are important in order to prioritize the investment of effort in improving the system. AIReSim also allows tuning of the knobs for achieving different tradeoffs in the system, as well as to consider various ``what-if'' scenarios. We present a case study of applying AIReSim for capacity planning for large-scale clusters running AI workloads.