Hybrid RANS/LES methods can produce more reliable results than RANS with a reasonable computational cost. Thus, they have the potential to become the next workhorse in the industry. However, in continuous approaches, whether or not they depend on the grid step explicitly, the ability of the model to switch to a well-resolved LES depends on the mesh generated by the user, such that the results are user-dependent. The present paper proposes a self-adaptive strategy, in which the RANS and LES zones are determined using physical criteria, in order to mitigate the user influence. Starting from an initial RANS computation, successive HTLES are carried out and the mesh is refined according to the criteria. To demonstrate the feasibility of this strategy, the method is applied to the backward-facing step case with the Hybrid Temporal Large Eddy Simulation (HTLES) approach, but is suitable for any other hybrid approach. The results obtained show that the method reaches a fixed point after only a few simulations and significantly improves the predictions when compared to RANS, with no intervention from the user. Even though the process is still a long way from being applicable to a wide range of turbulent flows, this paper is a demonstrator of the applicability of this self-adaptive strategy.