Edge-Native RAG — When the Small Stack Is the Right Stack
Every AI chatbot has the same problem: it forgets you.
You tell it your name in one conversation, and by the next one it has no idea who you're talking to. You tell it your preferences, and a week later it's making things up. The bigger you build, the worse this gets — and at some point the bill from your LLM provider stops being funny too.
The fix has a name: RAG, short for retrieval-augmented generation. You give the model a memory it can search before it answers. The catch is that the standard way to build it is overcomplicated — a vector database, an embedding service, a chat model API, and a server somewhere to glue them all together. Four services, four monthly bills, four different things that can wake you up at 3 AM. Three months in, you're paying close to $200 a month for what is, honestly, a chatbot that remembers things.