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In this blog post, I explore Amazon Q Developer’s latest enhancements to the IDE chat experience including increased context control, chat history and other conversation management features. On March 11th, 2025, my colleague published Take control of your code with Amazon Q Developer’s new context features detailing several improvements to the chat experience within VS Code. These included increased context transparency, the ability to select specific files or folders as context, prompt libraries for reusing prompts across conversations and projects, and project rules to help enforce coding standards and best practices across your teams.

Since then, Amazon Q Developer released additional features in VS Code to help provide users with more control over their conversations and enhance their ability to maintain development context across longer periods. These new capabilities make your interactions with Amazon Q Developer not just more efficient, but also more contextual and persistent—helping you maintain your development flow, even when work spans hours or days. Now, let’s jump in and explore some of the new features available today.

Conversation persistence

We’ve all been there—you’re deep in conversation with Amazon Q Developer, maybe you’re debugging an authentication problem, optimizing a complex database query, or designing a new API structure. You and Amazon Q Developer have been going back and forth, uncovering insights and piecing together solutions.

Then reality intervenes. You close your IDE to focus on another task, step away for a meeting, or maybe update your computer. When you finally return to your IDE, ready to dive back in, you’re met with a blank chat window. All that context, all those valuable exchanges—gone. You find yourself trying to reconstruct your train of thought, wasting precious time and momentum.

Amazon Q Developer now preserves your conversations across your IDE sessions. Instead of starting from scratch each time you open the IDE, you can now come back to your conversation and pick up right where you left off.

Conversation History Search

It isn’t just after a closed IDE session or coming back to your computer after a long-weekend that you want Amazon Q Developer to remember what you have been working on. Sometimes you need to reference a previous solution — maybe Amazon Q Developer gave you some good advice on optimizing your database queries that you want to use elsewhere, or maybe you decided to work on some front-end components so you could have fresh eyes for the API performance issue you’ve been working at.

Now, you can access your previous conversations with Amazon Q Developer by clicking on the search icon in the top right corner of your chat window. You can quickly locate specific discussions by typing keywords into the search bar, then either review the previous exchange or continue the conversation where you left off.

Screenshot of an Amazon Q interface showing a chat window. The user has requested to add Javadocs style comments to a Java file. The AI assistant is responding with an offer to help add Javadoc comments, mentioning a TODO for adding these comments and offering to provide a template.

Fig 1 – View chat history feature in the Amazon Q Developer VS Code chat interface.

Conversation Export

But what if you need to share these insights with a teammate or want to keep a local record for future reference? You can now easily export your chat sessions as markdown files, preserving all the valuable information for offline use or collaboration. To do this, click the export button located directly to the right of the chat history button. Alternatively, when browsing your chat history, you can export individual sessions by clicking the three dots on the right side of each conversation entry.

Screenshot of an Amazon Q chat interface showing a bulleted list of test requirements or specifications. The list includes items about using temporary files, testing success/failure scenarios, verifying edge cases and invalid inputs, testing public methods of a WordList class, and including cleanup annotation.

Fig 2 – Export chat feature in the Amazon Q Developer VS Code chat interface.

Increasing your control over context

Last July, we announced the ability to use @workspace in your chat session to provide comprehensive context across your entire application within the IDE. To provide more control over what Amazon Q Developer uses as context, earlier this year we released the ability to use the @ symbol in the chat to include specific folders or files as context for your conversation.

We are now taking your level of control one step further to allow you to use @ in your conversation to find and include classes, functions, and global variables into the input context. Rather than leaving it up to Amazon Q to determine the relevant files, folders, or functions for your request, you can continue to be more explicit with your request to receive the most relevant and accurate responses.

Screenshot of an Amazon Q interface showing a file search/navigation panel with "ja:" search query displaying Java-related files. The list includes various Java source files and configuration files from a Q-Words project, including controllers, models, and utility classes. At the bottom is a search input box with "@ja" and a note about Amazon Q Developer using generative AI.

Fig 3 – Context selection in Amazon Q Developer chat, using “@” to show relevant folders, files, functions.

Conclusion

Amazon Q Developer is continuing to evolve its features that help put developers in control of their coding experience. By offering conversation persistence, history search, and export capabilities, Amazon Q Developer works to create continuity that allows developers to maintain their momentum across sessions and easily revisit past solutions. The expanded context control features empower developers to fine-tune their interactions with Amazon Q Developer, and receive more precise and relevant responses.

To get started with these features in VS Code, visit the Amazon Q Developer Getting Started guide and explore the full range of capabilities that can help you create impressive software more efficiently.

Eva Knight

Eva Knight is a Worldwide Go-To-Market specialist at Amazon Web Services (AWS) focusing on generative AI across the software development lifecycle. Her journey at AWS began in 2022 as a Business Development Intern, transitioning to a full-time role after completing her Bachelor’s degree in Marketing and Information Systems from the University of Washington.

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