RAG is not new. Chunk a document, embed the chunks, store them in a vector database, run a retrieval step on each query, then feed the results to the model. Every team building with AI has wired this up at least once. It works, and it is also a stack of moving parts you have to assemble and keep running: a parser, an embedding model, a vector store, a retriever, and the glue between them.
Backboard does nothing novel here. It just puts the whole thing behind one API. Upload a file, wait for it to index, ask a question. Retrieval happens automatically inside the same send_message call you already use. The point is not a new idea, it is that it is all unified and easy.
Three steps
Upload a document to an assistant.
Wait for it to reach indexed status.
