GenAI App Architecture Explained (Part 1: The Big Picture)
The first time you interact with an app like ChatGPT, all you see is the chat box. It feels magical — you type, it answers. But for anyone in Ops or engineering, one question immediately comes up: what’s really happening behind the scenes? At the heart of every GenAI app lies an LLM (Large Language Model), but the LLM alone is just one piece of the puzzle. Around it sits an entire stack: orchestration layers, data pipelines, embeddings, vector databases, plugins, caches, observability tools, and guardrails. Together, they transform a “highly clever text generator” into a production-grade GenAI application. In this post, we’ll start with the big picture: the main components and flows that make an app like ChatGPT work. In the next parts, we’ll drill down into each layer (RAG, Ops monitoring, validation, etc.), highlighting both how they work and what usually breaks in production. Picture of what it feels like the first time we interact with a LLM: Which remind me of this famo...