Hi Khishamuddin, nice to meet you!
Not only you are facing this issue, it's a very common problem we face when we start with AI Generated or "Vibe" coding.
AI model are smart enough to generate precise and industry level code, but they need deep context, instructions and thought process.
Also it should not be one shot process, but a step by step approach. You can have a look at how Gemini CLI is doing. With this process, we can fix a problem one at a time and make our final product best as it can be.
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?