Overcoming Challenges in Building with AI

Building a project with AI tools like Cursor AI can be both a rewarding and complex experience.

During the creation of my Netflix Clone, I encountered several challenges that tested my problem-solving skills, despite the powerful assistance of AI.

I’ve documented the entire process in my YouTube tutorial, but here’s a closer look at the hurdles I faced and how I overcame them.

Key Challenges Faced

1. Understanding AI Suggestions
   AI tools like Cursor generate multiple potential solutions for a single coding issue. Deciding which suggestion best fits the project context required critical thinking. For example, while integrating TMDB API with React, Cursor provided numerous variations for handling asynchronous calls, which led to some initial confusion.
2. Customization Beyond AI’s Scope
   AI excels at automation but struggles with nuanced requirements. When customizing Firebase authentication for my Netflix Clone, I had to refine Cursor’s boilerplate suggestions to meet specific security standards and user flow requirements.
3. Debugging AI-Generated Code
   AI often generates complex, optimized code that can be difficult to debug. While implementing dynamic UI components with Tailwind CSS, I spent time deciphering Cursor’s suggestions to fix styling inconsistencies across different screen sizes.
4. AI Learning Curve
   AI tools require familiarity with their workflows and limitations. Initially, I spent time learning Cursor’s features, such as how to interact with its suggestions effectively and refine its outputs for more precise results.
5. Ensuring Scalability
   While AI sped up prototyping, ensuring the scalability of my Netflix Clone required careful manual intervention. Cursor’s code generation was optimized for immediate functionality but needed adjustment to handle larger datasets or future feature expansion.
6. Maintaining Code Readability
   AI-generated code can be difficult to read, especially when working in teams. Cursor’s suggestions for React hooks and complex state management sometimes resulted in less intuitive code structures, requiring additional refactoring for clarity.
7. Over-Reliance on AI
   It’s tempting to depend entirely on AI tools, but over-reliance can hinder skill growth. For tasks like API error handling or responsive design debugging, I ensured that I manually reviewed Cursor’s suggestions to strengthen my understanding.
8. Time Consumption in Fine-Tuning
   Although AI accelerates many processes, fine-tuning its outputs can be time-consuming. For instance, aligning the visual design of the Netflix Clone with Tailwind CSS required several iterations and manual adjustments to achieve the desired look and feel.

How AI Helped Overcome Challenges

Despite these challenges, AI tools like Cursor were invaluable. They reduced repetitive tasks, streamlined coding workflows, and provided innovative solutions that enhanced the project.

Cursor’s ability to generate React components, API integrations, and CSS layouts saved countless hours and allowed me to focus on higher-level tasks like optimizing the user experience.

If you want to explore how to build projects like this with AI, check out my full tutorial on YouTube. AI tools are transforming web development, and while challenges exist, they’re well worth overcoming.

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