RNA ribozyme (Walter Engelke, Biologist (London, England) 49:199–203, 2002) datasets typically contain from a few hundred to a few thousand naturally occurring sequences. However, the potential sequence space of RNA is huge. For example, the number of possible RNA sequences of length 150 nucleotides is approximately \( 1{0}^{90} \) , a figure that far surpasses the estimated number of atoms in the known universe, which is around \( 1{0}^{80} \) . This disparity highlights a vast realm of sequence variability that remains unexplored by natural evolution. In this context, generative models emerge as a powerful tool. Learning from existing natural instances, these models can create artificial variants that extend beyond the currently known sequences. In this chapter, we will go through the use of a generative model based on direct coupling analysis (DCA) (Russ et al., Science 369:440–445, 2020; Trinquier et al., Nat Commun 12:5800, 2021; Calvanese et al., Nucleic Acids Res 52(10):5465–5477, 2024) applied to the twister ribozyme RNA family with three key applications: generating artificial twister ribozymes, designing potentially functional mutations of a natural wild type, and predicting mutational effects.