Chatbot Development & RAG Systems
Document Intelligence RAG Assistant
A confidential client project — a Retrieval-Augmented Generation (RAG) system that answers user questions from long, complex documents (70–80+ pages), fetching the exact relevant information and returning clear answers.
- RAG
- Retrieval-Augmented Generation
- LLM
- Vector Database
- Embeddings
- Document Q&A
- NLP
- Python
Case study
The build, in detail
Overview
Delivered for a client, this is a Retrieval-Augmented Generation (RAG) assistant built to make long, dense documents easy to query. Official documents can run to 70–80 pages or more, and finding a specific answer inside them is slow and frustrating.
What it does
A user asks a question in plain language, and the assistant retrieves the most relevant passages from the source document and returns a precise, grounded answer. Instead of reading the whole document, users get exactly the information they need, on demand.
How it works
The assistant is built on a retrieval-augmented generation pipeline. The source document is indexed so that a natural-language question can be matched against the passages most likely to contain the answer, and only those passages are passed to the language model to compose the response.
Key engineering decisions
The system is grounded in the source material rather than relying on the model's general knowledge, so answers stay accurate and traceable back to the document they came from.
Inside the build
Screens from the system
Document Intelligence RAG Assistant — system architecture