AI-Assisted Capacity Planning Challenge | Designing Zomato with ChatGPT | Where AI Was Wrong

🚀 AI-Assisted Capacity Planning – Don’t Trust AI Blindly! | Uber System Design Case Study
In this video, we take a real-world application — Uber — and perform capacity planning using AI assistance.
But here’s the twist 👇
👉 We don’t just accept AI answers
👉 We break them, analyze them, and fix them like real engineers

🧠 What You’ll Learn
✅ How AI helps in system design
✅ QPS (Queries Per Second) calculation using AI
✅ Storage and server estimation
✅ Real-world scaling challenges
❌ Where AI goes wrong in capacity planning
❌ Why naive assumptions can break your system
🔧 How to correct AI output with proper engineering thinking

📊 Topics Covered

Capacity Planning for Uber-like system
Traffic estimation (QPS calculation)
Storage estimation (TB scale systems)
Server and infrastructure planning
Real-time systems (location tracking impact)
Microservices and distributed architecture
⚠️ Key Insight
👉 AI underestimates real-world complexity
👉 Most system load comes from hidden factors like real-time updates
👉 Engineers must validate and rethink AI outputs

🎯 Learning Outcome
💡 AI gives direction — Engineers ensure correctness
💡 Never blindly trust AI for system design decisions

📌 This Video is Perfect For:

System design beginners
Backend engineers
Interview preparation (FAANG / Product companies)
Anyone using AI for technical problem solving

#SystemDesign #Startup #FitnessApp #AITech #BackendEngineering #Scalability #TechInterview #DistributedSystems #AIStartup #CapacityPlanning

Видео AI-Assisted Capacity Planning Challenge | Designing Zomato with ChatGPT | Where AI Was Wrong канала Vaibhav Explains Tech