LLMs are stateless. Every API call to a raw model is a blank slate. The model has no idea what was said two messages ago. So the moment you want a chatbot that remembers the conversation, you are on the hook for state.
The usual answer is infrastructure. Spin up Postgres to store message history. Add Redis to cache sessions. Stand up a vector database for long-term memory. Write the code that loads history, trims it to fit the context window, stitches it into every prompt, and saves the new turn. That is a lot of plumbing before the bot says hello.
Backboard handles state for you. Two ideas replace the whole stack: threads and assistants. You never run a database.
The model
Three things, nested:
