Autoencoders are neural networks designed for data compression by encoding input into a latent space and decoding it back to its original form, but they struggle with generating diverse samples. Traditional autoencoders often produce sparse and disjointed latent spaces, limiting their ability to generalize and create varied outputs. Variational Autoencoders (VAEs) address these limitations by mapping inputs to a distribution rather than fixed points, allowing for smoother data representation and better generalization. VAEs consist of an encoder that outputs the mean and variance of a latent distribution, a reparameterization step for sampling, and a decoder to reconstruct inputs, incorporating a loss function combining reconstruction and KL divergence losses. This tutorial explores the setup, training, and technical implementation of VAEs using the Lance data format to optimize data handling, facilitating efficient and scalable workflows. By leveraging VAEs, users can achieve more diverse and meaningful data generation, pushing the boundaries of machine learning capabilities.