Ground RAG answers in your own documents

Every tutorial wires up a different framework, and I just want my chatbot to answer from my own files.

BeginnerRead 6 minDoing: a couple of hours, once you already have working access to a chat model and an embedding model7 steps

▶ Start

What you'll have at the end

A working script that answers three questions using only your own uploaded documents

Before you start

You need

A handful of your own documents saved as plain text, and code that can already call a chat model and turn text into an embedding vector.

Not covered

Improving retrieval quality with hybrid search or reranking, or handling a question with no good match; this only covers getting a first answer grounded in your own files.

Leans on

Turn your documents into something searchable

Turn each document into labeled plain text
Split each document into overlapping chunks
Turn every chunk into a vector
Store every chunk with its vector and label

Answer a real question from what you found

Score every chunk against the question
Limit the model to what you retrieved

Prove every answer traces back to your files

Run all three questions and check sources

Where to go next

Split your documents into chunks that help RAG give better answers

when a chunk boundary is quietly cutting a needed fact in half or mixing unrelated facts into one vector

Make your RAG system say it does not know instead of guessing

when a real question scores low against every chunk instead of landing cleanly like these three test questions did

Checked 15 Aug 2026

Part of the RAG (Retrieval Augmented Generation) cookbook