Grounded Docs Assistant – RAG Chatbot with Zero Hallucination

Grounded Docs Assistant – RAG Chatbot with Zero Hallucination

A Retrieval-Augmented Generation chatbot that answers strictly from a client’s own documentation, and refuses to guess. Every response is grounded in retrieved, citable source material with inline [n] citations, and when nothing relevant exists, the bot says so explicitly rather than inventing an answer. That “I don’t know” gate is the whole point: it’s the difference between a tool a business can trust in front of customers and one that quietly makes things up.

The system ingests multiple source types (PDFs, live documentation sites via sitemap, and OpenAPI/Swagger specs), then chunks, embeds, and indexes them for retrieval. At query time it runs a multi-stage pipeline: vector search for candidates, a local cross-encoder re-ranker to sharpen relevance, a confidence gate to decide whether to answer at all, and grounded generation with citations. Crucially, the entire retrieval trace is exposed, so any answer can be audited back to its source, and usage analytics surface the documentation gaps where users asked questions the docs couldn’t answer.

It ships as an embeddable widget, a React chat UI, and a JSON API, so it drops into an existing product or docs site with minimal work.