This chapter introduces new Verified and Explainable AI terminology to better describe such algorithms by pigeonholing them. The combined provision of three types of explainability (a priori, ad hoc, and ex post) as advocated by hybrid explainability helps with verification while improving efficiency results by better structuring the specification space and accuracy ones, by enabling both machine and human explainability of the data while providing structured and more machine-readable representation. These can then be contextualised in a general framework named General Explainable and Verifiable Artificial Intelligence (GEVAI) showcasing the possibility of combining already-existing algorithms in a full data-driven pipeline. This observation is backed up by the latest results on hybrid explainability, which outperform the current state-of-the-art results. Last, we pose some final challenges for extending GEVAI into a data science pipeline while drawing similarities with evolutionary data-aware microservice orchestration.