Document-based Q&A

A question-answering approach that retrieves and synthesizes information from specific documents rather than relying on a model’s pre-trained knowledge. Enables precise, context-aware responses grounded in source material.

Key Features

• Document-specific context: Answers derived exclusively from provided documents (e.g., e-books, internal docs)
• RAG foundation: Combines document retrieval with generative AI for accurate responses
• Agentic enhancement: Supports multi-step reasoning via agentic-rag workflows
• No general knowledge bias: Prevents hallucinations by restricting answers to source documents

Implementation Requirements

• Active azure-ai subscription
• Document ingestion pipeline (e.g., PDF/text processing)
• rag pipeline configuration with document metadata tagging
• Foundry integration for enterprise-grade deployment

Integration Notes

• Built using Azure AI services and Foundry (per Azure Innovation Station tutorial)
• Designed specifically for e-book knowledge bases
• Requires document-specific indexing for retrieval accuracy

Related Concepts

• agentic-rag
• azure-ai
• rag
• Document Retrieval
• Enterprise Knowledge Base

2026 04 14 Build an agentic rag system in azure ai and foundry