RAG

Document Assistant

Upload a PDF and ask questions answered strictly from the document. Responses are grounded in retrieved chunks, and sources are shown clearly so the experience feels trustworthy, inspectable, and production-ready rather than just conversational.

Open RAG Workspace View System Design

PDF Q&ADocument-grounded question answering

SourcesChunk and page references surfaced in UI

RAGRetrieval first, answer second

Workspace

Upload document

Add a PDF file to build a retrievable document context. The page is designed to make the upload state visible, predictable, and easy to scan.

PDF

Drop file here or choose file

Answers are generated only from indexed document chunks. This interface is optimized for clean upload flow and trustworthy retrieval.

PDF onlyChunked for retrievalSource-aware answers

No documents yet. Upload a PDF above.

Question interface

Ask a question about the document

Questions should be answered only from retrieved document evidence. This layout makes the ask-answer-source flow feel deliberate and professional.

Question

Document

Ask a question based on the document content

Answer

Grounded answer mode

Ask a question to see an answer grounded in the document.

Sources

No sources to display for this answer.

RAG system view

How this experience is meant to feel

A strong RAG page should communicate that the assistant is not inventing answers from memory. The interface should visually reinforce that retrieval, grounding, and evidence come first.

Upload LayerUsers add PDFs through a clear drag-and-drop or file selection workflow.

Indexing LayerThe document is chunked and embedded so the system can retrieve relevant text at query time.

Answer LayerThe assistant builds a response only from retrieved chunks rather than unrestricted model memory.

Source LayerPage and chunk references make answers inspectable and improve trust in the system.

Architecture

Document assistant pipeline

The design below is intentionally aligned with real RAG system architecture. The portfolio value comes from showing not only the interface, but also the reasoning behind how the system works.

1. Document ingestionUpload PDF, extract text, clean the content, and preserve metadata such as page references and document identity.

2. Chunking and embeddingSplit the document into retrieval-friendly chunks, generate embeddings, and save them into a vector index.

3. Retrieval workflowWhen the user asks a question, retrieve the most relevant chunks from the indexed document instead of scanning raw text every time.

4. Grounded generationPass only the retrieved evidence into the answer prompt so the response remains anchored to document content.

5. Source attributionReturn cited chunks and page references in the UI so the answer remains auditable and trustworthy.