Building a RAG chatbot has become a well-marked exercise: split the content into chunks, compute embeddings, search by similarity, hand the relevant passages to a model, generate the answer. It's an increasingly well-documented craft, built on solid pieces. We did it for GoodBarber apps: a chatbot that answers users' questions from the content published in the app — articles, events, points of interest — and not from the model's general memory. That part, the documented part, was by far the e...