Do not index
Do not index
AI tools make writing integration code faster than ever. Cursor Composer, for example, can turn API docs into working code with minimal effort. But integrations don’t stop at launch. Without proper planning, maintenance can bring bugs, rising support costs, and frustrated users. AI can simplify testing and improve reliability, making maintenance more manageable. Here’s how I use AI to create better tests—and how you can do the same.
1. Start with critical workflows
The first step is identifying the workflows users rely on most. These are the actions that deliver the core value of your integration and, if broken, disrupt the experience. In my video and audio bundle, the key workflows include creating video rooms, updating rooms, and deleting rooms. I write tests for each to cover core functionality and edge cases.
Focusing on these workflows first ensures that your tests address the most impactful areas and highlight potential gaps early. Once these workflows are covered, you can expand to secondary features.
2. Structure the prompt
Start with a clear and detailed prompt for your test. The goal is to guide the AI to write a test for a specific workflow. For example, when testing the "delete room" workflow in my video integration bundle, I start with:
Initial Prompt:
Create a test for the delete room workflow (@the file). This test should create a sample room and then delete it.
After developing a solid testing format, I use that initial test as a reference for subsequent prompts. This helps ensure consistency across tests. A follow-up prompt might be:
Subsequent prompt:
Create a test for the update room workflow. Use the structure of @test-example.test.js as a reference. Refining your prompts helps build reusable templates that save time while ensuring consistent tests. Cursor makes this process even faster by allowing you to reference files directly in your prompts
3. Iterate and Improve with AI
Once you have a working test, you can refine it further by using AI to identify gaps or edge cases.
For example, I asked Cursor:
"What edge cases might be missing from this test for delete room?"
The AI suggested testing scenarios like invalid permissions and API timeouts. Iterating in this way strengthens your tests, ensures you cover potential failures, and surfaces improvements for your core code.
Integrations don’t just need to work at launch—they need to remain reliable over time. AI tools make this easier, from generating initial test templates to identifying edge cases. These practices have helped me build integrations users can depend on.
And that’s it! What do you use? Feel free to share your thoughts. For more of this kind of content, subscribe to my newsletter. I write about building integrations and the AI-powered systems that help me build better.
.png?table=block&id=9ba33ac6-8e12-48f6-b980-4333b612ec56&cache=v2)