Undergraduate Teaching Assistants (TA) can be a dynamic resource for any Computer Science (CS) program. However, increasing the TA pool size is often one of the only methods explored when investigating department TA improvements, and budgetary restrictions frequently negate that possibility. Additionally, undergraduate students applying to TA for classes do not typically come from an educational background or have teaching foundations. We investigate alternative ways to improve TA skill sets to the department’s benefit in TA recruitment, refinement, and retention. At our institution, only 34% of Fall 2021 CS TAs continued as TAs for the Fall 2022 term. Even with the expected natural turnover of graduation and other life or academic factors, the low retention rate points towards an area that can be improved to benefit the program overall. Other institutions notice preparation issues with TA skill sets that lead to poor instruction, such as Patitsas’ observations and research at the University of Toronto. Improving TA skill sets and self-efficacy through TA refinement will indirectly help future recruitment and TA retention. Our research provides eight interactive modules inspired by existing programs at several North American universities. Anecdotal evidence utilized during module development suggests potential improvements to TA skill sets and TA self-efficacy. Module refinement and further anecdotal evidence are ongoing as we develop pathways to evaluate TA skill sets and self-efficacy improvements from our module implementations and others in the training program using a pre-test and post-test quasi-experimental methodology.