A software vendor wants to boost developer productivity with AI. Which of these choices best achieves that goal?

Integrating code-recommendation tools directly into developers' Integrated Development Environments (IDEs) and existing toolchains provides real-time assistance, suggesting code snippets, completing lines, and offering best practices as developers write code. This directly streamlines the coding process, reduces manual effort, and helps prevent common errors, thereby significantly boosting productivity. Training a binary classification model for code reviews is reactive; it happens after code is written, not during the development process. Deploying a code-forecasting system for defects is also reactive, identifying potential issues rather than aiding in the immediate creation of code. Automatically generating code with an NLP model can be powerful but often requires significant oversight, refinement, and integration effort, and may not be as universally applicable or immediately productivity-boosting for all coding tasks as real-time recommendations.