AI Document Assistant (RAG Demo)
A demo knowledge assistant that answers questions grounded in a set of internal documents, with citations back to the source — built to show our RAG implementation approach.
The challenge
Generic LLM chatbots confidently answer questions using only their general training data, which means they're often wrong about anything specific to an organization's own documents, policies, or product details.
Our approach
We built a retrieval-augmented generation (RAG) pipeline: documents are chunked, embedded, and stored in a vector database; at query time, relevant chunks are retrieved and passed to the model alongside the question, with the response required to cite its source passages.
The solution
The demo assistant answers questions strictly from the provided document set, shows which source passage each answer is based on, and explicitly declines to answer when the documents don't contain relevant information — rather than guessing.
The outcome
This is a demo prototype, evaluated internally against a test set of questions with known correct answers from the source documents, used to validate our RAG approach before applying it to client use cases.
Technology used
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