How does Gibbs sampling work in Latent Dirichlet Allocation? It seems that only one sample is sampled from the distribution of corpus topics

I'm very new to both Gibbs sampling and LDA. Currently, I'm trying to understand the collapsed Gibbs sampling method in LDA. However, I'm quite confused about the whole method when reviewing the algorithm. Here are my confusions and I badly need some guidance. For short, let $W$ be the set of all the words in the corpus, $Z$ is the set of topics respect to them, and $\theta$ is the set of tunable hyperparameters. In LDA, the algorithm seems to try to sample from the distribution $p(Z | W; \theta)$ . After the burn-in period, we get one sample $Z^\star \in p(Z | W; \theta)$ . Am I right? If yes, why does the Gibbs sampling method work since there is only one sample $Z^\star$ that is sampled from the distribution? If I'm wrong, I badly want some detailed explainations. The question may be naive, but I really need help.