Reconstruction error refers to the difference between the original data and its reconstructed version after processing through a model, such as in machine learning or data compression. It measures how well the model can recreate the input data, with lower errors indicating better performance. In practical terms, reconstruction error is often used in autoencoders, a type of neural network designed to learn efficient representations of data. By minimizing this error during training, the model improves its ability to capture essential features, making it useful for tasks like image denoising or anomaly detection.