Imagine your AI app goes viral overnight.

Thousands of users start sending the same prompts. Every request hits the AI model, your API costs explode, your server slows down, and your startup starts losing money.

That's why production AI systems never send every request directly to an LLM.

In this final lesson of Phase 3 (AI Backend Engineering), we build a production-ready AI Chat Backend using NestJS, Claude Code, and Gemini 2.5 Flash, implementing Caching and Rate Limiting exactly like modern AI products.

What you'll learn:
What is Caching?
Cache Hit vs Cache Miss
AI Cost Reduction Strategies
In-Memory Cache with NestJS
Cache TTL Explained
API Rate Limiting
Protect AI APIs from Spam & Abuse
Build a Production AI Chat Backend
Gemini 2.5 Flash Integration
Claude Code Workflow
Thunder Client Testing
Production Backend Architecture & Scaling Basics

Learn how real AI engineers build AI backends that are fast, secure, scalable, and cost-efficient.

Subscribe to The Techzeen and become a Production-Ready AI Engineer.

CONNECT & LEARN WITH US
Visit Our Website:
https://www.thetechzeen.com/

Source Code:
https://github.com/farzeen-ali/Cache-Rate-Limiting-NestJS-App

Full Playlist (AI ENGINEER 360°):
https://www.youtube.com/playlist?list=PL5OhSdfH4uDtyUmg0r-F5UeQ9sineyE9t

Get Lecture Notes (GitHub):
https://github.com/farzeen-ali/AI-Engineer-360

Join Techzeen AI WhatsApp Community:
https://whatsapp.com/channel/0029VbD3dLD5vKA2UgUCpX19

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