An attention mechanism is a technique in neural networks that focuses on specific parts of input data (e.g., in transformers) to improve performance on tasks like translation. It allows models to selectively focus on relevant information, similar to human attention.

NYD Application: Enables our Claude integrations to better understand context in long conversations and focus on relevant code sections during analysis.

Example: "The attention mechanism helped our model focus on the most relevant parts of the codebase when generating documentation."