A Neural Radiance Field (NeRF) is capable of representing scenes by capturing view-dependent properties from a specific set of images through neural network training. The lack of a significant initial image set can lead to a subsequent photographing session and training to improve the final view synthesis. For this purpose, we introduce a new variant of NeRF training analysis, termed the Neural Error Field (NEF). NEF visualizes and identifies view-dependent errors to reduce the number of ray samples used in the follow-up training. NEF does not require modifications to the NeRF core and training process. We evaluate and verify the accuracy of the results achieved with NEF on several public datasets, including real and synthetic images, and bounded and unbounded scenes.