// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
Early Stopping
A technique used to prevent a machine learning model from overfitting by stopping the training process when the model's performance on a separate validation set starts to worsen.
TECHNICAL DEFINITION
A regularization technique applied during iterative model training, particularly in neural networks, where training is halted when the model's performance on a designated validation set ceases to improve or begins to degrade, thereby preventing overfitting and optimizing generalization.
BACKGROUND
Prompt engineering is the process of structuring natural language inputs to produce specified outputs from a generative artificial intelligence (GenAI) model. Context engineering is the related area of software engineering that focuses on the management of non-prompt contexts supplied to the GenAI model, such as metadata, API tools, and tokens.
READ MORE ON WIKIPEDIASYNONYMS & ALIASES
- Stop training early
- regularization by stopping
- validation-based stopping
USAGE NOTE
Early stopping is a common practice to find the optimal point in a model's training without excessive computation.