Cite
In developmentPersonal project exploring agentic RAG. Every answer is grounded by inline citations; clicking one opens the source document at the exact region that produced it.
What it does
Teams upload their documents — PDFs, Word, Markdown, transcripts — and chat with them in natural language. Every answer is grounded by inline citations. Clicking a citation opens the source document at the exact region that produced the answer, with the relevant span highlighted.
Why I built it
Not a product — a personal project. RAG chatbots without citation are unfalsifiable. Citation grounding makes the model’s output checkable, which is the whole point of using a retrieval system. I also wanted to design a UI where the citation is the primary interaction, not a footnote — the document viewer is co-equal with the chat.
Status
Under active development. The retrieval pipeline and chat UI are working end-to-end; the document viewer’s region-anchor logic is the current focus.
Questions
What is Cite?
Cite is an agentic retrieval-augmented chat application. Teams upload their documents and ask questions in natural language. Every answer is grounded by inline citations — clicking one opens the source document at the exact region that produced the answer, with the relevant span highlighted.
How is Cite different from a typical RAG chatbot?
Two things. First, the retrieval step is agentic — the model can plan and iterate retrievals before answering, rather than a single top-k similarity lookup. Second, the citation is the user interface, not a footnote. Citations are clickable handles that synchronously scroll the document viewer to the cited span, so verification is one click instead of a search.