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January 07.2026
2 Minutes Read

Unlocking Code Clarity: How to Use Rovo Dev for Refactoring UI Components

Minimal UI table layout for refactoring using Rovo Dev.

Leveraging Incremental Prompts with Rovo Dev for More Effective UI Refactoring

In the world of software development, efficient refactoring of user interface (UI) components can be a game changer. Rovo Dev’s innovative multi-stage prompting system is designed to optimize the process, particularly when working with large language models (LLMs). Traditional methods often lead to confusion and complications when they attempt to tackle multiple tasks all at once, causing models to miss critical requirements or deliver inconsistent results. By implementing an incremental approach using Rovo Dev, developers can expect clearer outcomes and a significantly smoother workflow.

Why Incremental Prompts Are Essential

Large language models excel when given clear, specific instructions. When developers ask models to act on several requests at once, they risk obscuring intent. For instance, prompts that request to “refactor this component, add new features, and update documentation” often lead to loss of focus, unintended modifications, and inflated diffs that are challenging to review. Breaking tasks down into manageable, focused prompts simplifies communication with the LLM and ensures that each change can be validated separately, resulting in a cleaner, easier-to-manage codebase.

A Step-by-Step Guide to Using Rovo Dev

By revisiting a previous project, we can illustrate the effectiveness of Rovo Dev’s approach through a series of four concrete stages in refactoring a UI component. For example, the initial component, Component A, displays a dropdown menu with static links, which is then combined into a table displayed in Component B:

  • Stage 1: Refactor Component A to enable dynamic generation of dropdown items.
  • Stage 2: Integrate Component A into Component B, ensuring correct data is passed as props.
  • Stage 3: Simplify and enhance how identifiers are managed while validating all changes with tests.
  • Stage 4: Update mock data to reflect changes accurately, ensuring continued reliability in tests.

This method not only enhances focus but helps developers easily validate and implement changes, making each commit manageable.

Insights from the Rovo Dev Experience

The experiences from using Rovo Dev's prompts confirm that incremental prompting leads to more accurate results. Short prompts aligned with specific tasks reduce assumptions made by the language model, allowing for precise and deliberate outcomes. This aligns with the principles of Agile methodologies which stress clarity and sequence in task management. As developers leverage these advanced techniques, they should embrace the potential for refined teamwork and higher-quality outputs in software development.

For developers eager to improve their coding practices and refine their workflows, experimenting with Rovo Dev’s multi-stage prompts not only equips them with a powerful tool for refactoring but also fosters a culture of agile learning and adaptation.

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06.24.2026

Three Distinct Ways Atlassian's Learning Team Uses AI Effectively

Update Exploring AI Integration at Atlassian's Learning Team As organizations continue embracing artificial intelligence (AI) in various sectors, many are looking to successful case studies to refine their own AI strategies. One such example is Atlassian’s Learning Team, a pioneering group that exemplifies how a thoughtful integration of AI can optimize workflows while maintaining human creativity. In a recent exploration, they highlighted three key methods through which AI enhances their operations, alongside a clear stance on limits to its use. Saving Time with AI-Powered Research The first major advantage observed by the Learning Team is how AI dramatically reduces the time spent on research. Traditionally, gathering the information necessary for effective course design would range from several days to weeks. However, with the introduction of tools like Rovo Deep Research, they can command the AI to sift through vast internal documents to uncover insights quickly. Senior Learning Content Designer Michelle Cacciapaglia states, "Before, I knew all the research I needed existed, but finding, consuming, and synthesizing it took a lot of time. Now it happens with a prompt and a little back-and-forth." This efficiency not only benefits team members but also ensures that deep company knowledge is leveraged effectively without interrupting everyday workflows. Streamlining Administrative Tasks Another innovative use of AI is in managing administrative tasks that come with course planning. In their kick-off meetings, the team frequently deals with decisions that require written documentation which, when done manually, disrupts the flow of ideas. To mitigate this, they have implemented the Loom notetaker, which records meetings to generate summaries automatically. This allows team members to remain engaged in discussions, free from the distraction of note-taking, showcasing how technology can actively support collaborative efforts and retain team focus. Empowering the Writing Process When it comes to creating written content, many face the daunting challenge of the blank page. Atlassian’s Learning Team has effectively utilized AI tools to overcome this hurdle. By allowing Rovo to generate initial drafts based on their inputs, team members can kickstart their writing process without relinquishing total control over the content. A designer shared, "Rovo’s helping me build my confidence with writing because I’m getting better at looking at something and knowing whether it’s good or I need to give Rovo some more direction and try again." Through this collaborative approach, the team maintains a balance of human expertise with automated support. Holding Strong to Human Expertise Despite these technological integrations, the Learning Team maintains a clear boundary regarding AI usage. They consciously refrain from using AI for tasks where human expertise is paramount, such as creating presentation slides or crafting instructional content where personal touch is essential. Senior Team Manager Garrett Marttinen emphasized, "The people on our team are the ones making decisions about what we create and how it shows up." By embedding AI thoughtfully into their workflows rather than blindly adopting it, the team exemplifies a balanced approach to technology: leveraging advancements while prioritizing the irreplaceable value of human creativity and judgment. The Road Ahead: Rethinking Human-Centric Work Ultimately, Atlassian’s Learning Team serves as a potent reminder of the harmonious relationship between AI and human creativity. As they continue refining their AI integration process, they shed light on how technology can facilitate, rather than hinder, essential tasks within the workplace. As stated by Marshall Lee, Head of Customer Education: "AI isn’t just helping us move faster; it’s creating opportunities to reimagine how distinctly human capabilities—creativity, judgment, and hands-on collaboration—can be applied to deliver value." Such insights are invaluable as organizations navigate the challenges of adopting AI in a manner that supports sustained growth and innovation.

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Discover How AI Teammates on Jira Transform Your Agile Workflow

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