Hello Mihir! I'm truly excited to connect with you. I have the PRO subscription for Google AI Studio, and I'm looking to build scalable SaaS models using a low-code/no-code (or "vibe coding") approach. My strengths lie in a strong design sense and solid execution strategies, and I have a backlog of innovative ideas ready to go. My preferred stack involves Firebase for the backend infrastructure. The key challenge I face is with the AI itself: while I've been proficient in prompt engineering for the last two years, I've noticed a recent decline, with the models often becoming "lazy" or generic, which hinders my SaaS concept execution. My core question is this: What are the best practices, architectural patterns, and PRO-specific advantages I can leverage to robustly and effectively integrate the Gemini API with Firebase (especially Cloud Functions/Firestore) to overcome this "lazy AI" problem and reliably deliver high-quality, non-generic output for my SaaS applications?

Well organized, great!

Thanks Jym Cheong , Very helpful article.