Automatically wrap your code in feature flags with Unleash and Cursor

AI-assisted coding tools are changing the way we build software. They can help you write, test, and refactor faster than ever. But when it comes to shipping new features responsibly, even the smartest assistant can use some guardrails.

That’s where Unleash comes in. By pairing Unleash with Cursor, you can make sure your AI-generated code always follows your team’s feature flagging best practices, without having to repeat yourself in every PR review.

In this post, we’ll walk through how to set up Unleash as a “first-class citizen” inside Cursor, using official documentation as your AI’s data source and enforcing feature flag discipline through system-level rules.

Why use Unleash with an AI coding assistant?

AI tools like Cursor are incredibly fast at writing code, but they don’t automatically know your team’s rules around experimentation, rollout strategies, or safety nets. Unleash fills that gap. It gives you:

• A central place to manage feature flags and rollout states.
• Strong conventions for how to introduce and remove flags.
• Contextual guidance your AI assistant can reference when coding.

With a little setup, you can make Cursor “aware” of these standards so that every major feature is wrapped in a flag by default.

Step 1: Point Cursor to Unleash’s documentation

Start by giving Cursor access to the Unleash documentation. Cursor allows you to define external data sources, and Unleash’s docs are an excellent one to include. They cover everything from SDK setup to gradual rollouts and flag lifecycles.

In practice, you’ll want to include the following sections as your core context:

• Getting Started for quick setup instructions
• Feature Flag Best Practices for your AI’s default “ruleset”
• SDK Guides so Cursor knows how feature flags are actually implemented in your tech stack
• API Reference to create, update, and delete feature flags

Once this is indexed, Cursor can pull examples and best practices directly from official sources when it generates code.

 

Step 2: Create a system-level rule in Cursor

Cursor supports Project Rules and User Rules to help you influence the AI’s behavior. You can use this feature to encode your team’s Unleash standards.

Here’s an example system prompt you might set:

Unleash rules:
- Always follow Unleash feature flag best practices
  when proposing or generating new code.
- For every major feature or code change, wrap the functionality
  in a new flag using the Unleash SDK for the relevant language.
- Propose a meaningful flag name, if not already specified.
- When a small change might not need a flag, ask for confirmation.
- Reference official Unleash documentation for naming conventions,
  gradual rollouts, and cleanup lifecycle management.
- Use the Unleash API to fetch or create feature flags, 
  ask for authentication when needed
- Use this instance URL: {YOUR_INSTANCE_URL}
- Use the "{PROJECT_NAME}" project, unless otherwise specified.
- If a feature flag already exists for a related functionality,
  reuse it instead of creating duplicates. 
- Do not commit code that enables a new feature without a flag.

This ensures that Cursor doesn’t just generate code. It will generate safe code that aligns with how your team ships.

 

Company-wide and team-wide best practices

You can also take this opportunity to define how your AI assistant should work with your team’s specific SDK setup or internal conventions.

For example, if your organization maintains a shared flags file, include a line in your rule explaining where to define and how to import new flags. If you’ve built an internal SDK wrapper around Unleash to standardize metrics or targeting, describe that pattern here too.

This is where you can encode all of your company-wide best practices such as naming conventions, default strategies, cleanup responsibilities, or testing guidelines – so that Cursor consistently follows them across every project.

Step 3: Use Unleash concepts in your day-to-day prompts

Once your environment is set up, you can start using Unleash-aware prompts in your workflow. Here are a few examples that work well:

Creating a new feature:

“Add a new payment retry mechanism wrapped in a feature flag using Unleash.
Follow our team’s naming conventions and SDK setup.”

Testing a feature rollout:

“Show how to use Unleash’s gradual rollout for a new flag
called payment-retry-v2 in Node.js.”

Cleaning up flags:

“Remove the beta-dashboard feature flag.
The feature should now be permanently enabled.”

Cursor will use the Unleash docs as a reference to produce consistent, standards-compliant results.

Step 4: Keep the context lightweight

When you start integrating Unleash into AI-assisted workflows, it’s tempting to overload your prompts with every possible detail. In our testing, shorter context worked better.

Cursor models respond well to clear, simple instructions like:

“Wrap all new user-facing features in Unleash flags.
Use the docs to ensure correct syntax.”

This approach helps Cursor stay flexible while still following your rules.

Step 5: Validate and refine

Once you have Cursor generating Unleash-integrated code, it’s worth running a few small experiments:

• Compare AI-generated flags to your existing ones. Are names consistent?
• Check if the model is suggesting gradual rollouts where appropriate.
• Confirm that cleanup instructions are being followed when removing flags.

If something’s off, adjust your system rule or add a short clarification. Over time, you’ll find the right balance between control and creativity.

What makes this combination powerful

Pairing Cursor and Unleash gives you the best of both worlds:

• AI speed for coding and iteration
• Feature flag discipline to ensure safety and gradual rollouts

Your AI assistant effectively becomes an “Unleash-native” developer who always applies FeatureOps best practices. It learns to treat feature flags as a non-negotiable part of your development process, not an afterthought.

What about other AI coding assistants?

While this post focuses on Cursor, the same general approach applies to other AI-assisted development tools. Each platform has slightly different ways of handling persistent context and external documentation, but the goal is the same: teach your assistant to treat Unleash best practices as part of your engineering culture.

GitHub Copilot

If you’re using GitHub Copilot, for example, you can’t define system-level prompts in the same way, but you can use Copilot Custom Instructions or repository-level configuration files to embed your rules. Including short Unleash-specific reminders in comments or README files also helps reinforce the pattern across PRs.

JetBrains Junie

For JetBrains Junie, the setup can be managed through its AI Profiles. You can create a custom “profile” that includes a rule similar to your Cursor system prompt, pointing Junie to Unleash documentation or your internal playbook for feature flagging. This approach works well if you’re using JetBrains IDEs like IntelliJ or WebStorm.

Aider and Windsurf

Aider and Windsurf both support contextual prompts and file-based configuration. In these cases, you can store your Unleash rule in a .aider.conf or workspace instruction file. Just make sure to include links to the same documentation resources, and keep the Unleash SDK examples close to the repo so the assistant can reference them easily.

The key difference across tools is how persistent context is managed. Cursor keeps rules at the workspace level, while others rely on inline context, configuration files, or environment variables. With a few small tweaks, you can achieve the same outcome anywhere: a coding assistant that automatically respects Unleash’s best practices and wraps every new feature behind a flag.

Final thoughts

This setup won’t magically make your AI perfect, but it does help it think like a responsible engineer. The real value here is consistency: every new major feature automatically starts behind a flag, and you’re using the same trusted Unleash patterns across all AI-generated work.

If you’ve been experimenting with AI-assisted development and want to make it safer and more maintainable, integrating Unleash with Cursor is a solid next step. And if you discover clever ways to extend this workflow, the Unleash community would love to hear about it.

FAQs

1. How does Unleash improve AI-assisted software development?
   Unleash adds structure and safety to AI-generated code by managing feature flags across your environments. When paired with tools like Cursor, it helps you wrap every new feature in a flag by default. This helps maintain consistent rollout practices, limits exposure to risky changes, and supports teams using AI pair programming or automated code generation.
2. What are the main benefits of combining Unleash with AI coding tools?
   By integrating Unleash with AI assistants such as Cursor, Copilot, or Junie, you can enforce feature flag best practices automatically. This setup allows your team to safely experiment, run gradual rollouts, and reduce deployment risk, all while letting AI handle repetitive implementation work. The result is a faster, safer continuous delivery process.
3. Can AI tools like Cursor or Copilot handle feature flag lifecycles automatically?
   Most AI coding assistants can create or remove flags when prompted, but full feature flag lifecycle management is best handled by Unleash itself, including cleanup, rollout tracking, and retirement. That’s why connecting your AI workflow with the Unleash API or SDK is key to achieving a truly automated, AI-assisted DevOps pipeline.
4. How do feature flags help reduce technical debt?
   Feature flags help teams ship changes incrementally and remove unused code when experiments end. When AI tools automatically follow this model, your team avoids accumulating “zombie flags” and unused code paths. Combined with Unleash’s cleanup reminders and clear ownership tracking, this approach keeps your codebase clean and maintainable.
5. Can I use this Unleash + AI setup in CI/CD pipelines?
   Absolutely. Unleash integrates with most CI/CD systems through its REST API, allowing you to create, update, and evaluate feature flags automatically during builds or deployments. When you include this in your AI-driven CI/CD workflow, your assistant can propose new flags, register them in Unleash, and even suggest cleanup PRs when features stabilize.
6. What other tools support this type of setup?
   Beyond Cursor, similar configurations work with GitHub Copilot, JetBrains Junie, Aider, and Windsurf. The main difference lies in how each tool stores persistent instructions. Cursor uses system-level rules, while others rely on repository configuration files or IDE profiles. In every case, the goal is the same: embed your Unleash best practices directly into your AI assistant’s workflow.
7. Is this approach useful for progressive delivery or A/B testing?
   Yes. Unleash is built for progressive delivery, and its feature flags can be used for A/B testing, canary releases, or controlled rollouts. When your AI coding assistant understands how to implement these patterns, it can generate experiment-ready code that follows your organization’s rollout strategy automatically.