How does a ChatGPT-like system actually work behind the scenes?
When you type a message into an AI chatbot, it looks simple:
You → AI → Response
But a production system is much more interesting.
A simplified flow looks something like:
User → API Gateway → Authentication / Rate Limiting → AI Orchestrator → Context & Memory → LLM → Safety / Processing → Streaming Response
And once thousands or millions of users start sending requests simultaneously, the difficult part isn't just calling an LLM API.
You have to think about:
• How requests are distributed
• How conversation history is stored
• How much context gets sent to the model
• How responses are streamed token-by-token
• What happens when the model/provider fails
• How rate limits and queues are handled
• How latency and cost are controlled
• How the system scales as traffic increases
That's what makes AI system design interesting.
I recently made Episode 2 of my System Design in the AI Era series explaining a ChatGPT-like architecture from request → model → response.
If you're interested in the full visual breakdown, here's the video:
https://youtu.be/CxmhPKvX49A?si=MvLcUXqMsMPmNB85
I'm also curious: what part of a ChatGPT-like architecture would you consider the biggest scaling bottleneck?