AWS DevOps & Developer Productivity Blog

Category: Artificial Intelligence

Expanded resource awareness in Amazon Q Developer

Expanded resource awareness in Amazon Q Developer

Recently, Amazon Q Developer announced expanded support for account resource awareness with Amazon Q in the AWS Management Console along with the general availability of Amazon Q Developer in AWS Chatbot, enabling you to ask questions from Microsoft Teams or Slack. Additionally, Amazon Q will now provide context-aware assistance for your questions about resources in your account […]

Leverage Amazon Q Developer and AWS Chatbot within Slack

The release of Amazon Q Developer and its ability to be integrated into AWS Chatbot allows users who use Microsoft Teams or Slack to stay within their communication platform and interact with a conversational generative artificial intelligence (AI) AWS expert. Amazon Q Developer is a conversational generative AI chatbot that provides AWS assistance in the form of […]

Introducing the next-level of AI-powered workflows with Amazon Q Developer inline chat

Earlier today, Amazon Q Developer announced support for inline chat. Inline chat combines the benefits of in-IDE chat with the ability to directly update code, allowing developers to describe issues or ideas directly in the code editor, and receive AI-generated responses that are seamlessly integrated into their codebase. In this post, I will introduce the […]

Introducing the new Amazon Q Developer experience in AWS Lambda

AWS Lambda recently announced a new code editor based on Code-OSS. Like the previous version, the new editor includes Amazon Q Developer. Amazon Q Developer is a generative AI-powered assistant for software development that can help you build and debug Lambda functions more quickly. In this post, I provide an overview of Amazon Q Developer’s […]

Exploring Telemetry Events in Amazon Q Developer

As organizations increasingly adopt Amazon Q Developer, understanding how developers use it is essential. Diving into specific telemetry events and user-level data clarifies how users interact with Amazon Q Developer, offering insights into feature usage and developer behaviors. This granular view, accessible through logs, is vital for identifying trends, optimizing performance, and enhancing the overall […]

Reinventing the Amazon Q Developer agent for software development

Amazon Q Developer is the most capable AI-powered assistant for software development that reimagines the experience across the entire software development lifecycle, making it easier and faster to build, secure, manage, and optimize applications on AWS. Using your natural language input and your project context, Amazon Q Developer’s agent for software development autonomously implements multi-file […]

Best Practices for Working with Pull Requests in Amazon CodeCatalyst

Best Practices for working with Pull Requests in Amazon CodeCatalyst

According to the Well-Architected DevOps Guidance, “A peer review process for code changes is a strategy for ensuring code quality and shared responsibility. To support separation of duties in a DevOps environment, every change should be reviewed and approved by at least one other person before merging.” Development teams often implement the peer review process […]

Accessing Amazon Q Developer using Microsoft Entra ID and VS Code to accelerate development

Overview In this blog post, I’ll explain how to use a Microsoft Entra ID and Visual Studio Code editor to access Amazon Q developer service and speed up your development. Additionally, I’ll explain how to minimize the time spent on repetitive tasks and quickly integrate users from external identity sources so they can immediately use […]

How A/B Testing and Multi-Model Hosting Accelerate Generative AI Feature Development in Amazon Q

How A/B Testing and Multi-Model Hosting Accelerate Generative AI Feature Development in Amazon Q

Introduction In the rapidly evolving landscape of Generative AI, the ability to deploy and iterate on features quickly and reliably is paramount. We, the Amazon Q Developer service team, relied on several offline and online testing methods, such as evaluating models on datasets, to gauge improvements. Once positive results are observed, features were rolled out […]