Amazon SageMaker AI enhances Amazon Q Developer with custom code suggestions and workspace context

Today, AWS announces significant enhancements to Amazon Q Developer in Amazon SageMaker AI Jupyter Lab, introducing customization of code suggestions based on private code repositories and the ability to include entire workspace context for improved code assistance. These new features empower organizations to leverage their proprietary code and improve the relevance of code suggestions, ultimately enhancing developer productivity and code quality within Jupyter Lab environments.

With the new customization feature, Amazon Q Developer can now assist with software development in ways that conform to your team's internal libraries, proprietary algorithmic techniques, and enterprise code style. An Amazon Q Developer customization is a set of elements that enables Amazon Q to provide suggestions based on your company's code-base. This ensures that code suggestions, both inline and chat based, align perfectly with your organization's specific coding practices and standards.

Additionally, the workspace context enables Amazon Q Developer to locate files, understand how code is used across multiple files, and generate code that leverages multiple files, including those that aren't currently opened. This contextual awareness results in more accurate and relevant code assistance, helping developers better understand their entire project structure before they start coding. Users can access the workspace features through the chat interface, ensuring a seamless development experience that takes into account the full scope of their project.

These enhancements to Amazon Q Developer in Amazon SageMaker AI Jupyter Lab are now available in All Regions where Amazon SageMaker AI is offered.

To learn more about these new features see documentation.
 

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