Document RAG Assistant for Grounded Q&A

Build document Q&A systems that retrieve relevant context first and generate answers grounded in source material.

Categories

Tech Used

Problem

Teams need reliable access to internal knowledge, but plain LLM chat can return unsupported answers or miss document-specific context.

Approach

• Designed chunking, metadata, and retrieval strategies for private document collections
• Constrained answer generation around retrieved context to improve grounding
• Added user-facing formatting and guardrails for clearer, more trustworthy responses

Results

• Reusable RAG architecture for document assistants and internal knowledge bots
• More transparent answers supported by relevant retrieved context

Demo Videos