Case study
AI Document Assistant
Document search and question answering using embeddings and generative AI, with human review.
The challenge
- Relevant information was spread across many documents and hard to search quickly.
- Any AI-generated answer needed to be grounded in real source content, not invented.
- The demonstration had to show a safe pattern with human review in the loop.
The approach
- Built a portfolio demonstration to show the pattern rather than a production client deployment.
- Indexed documents using embeddings so relevant passages could be retrieved.
- Used a generative model to draft answers grounded in retrieved passages, with a human review step.
The solution
- Document indexing and retrieval using embeddings and search.
- Generative answers grounded in retrieved source passages.
- A review step so a person confirms answers before they are used.
Outcome
- The demonstration showed relevant passages surfaced quickly from a document set.
- Answers stayed grounded in real content with human review before use.
- It illustrates a safe, practical pattern rather than a live client system.
Technologies
- Azure AI Search
- Azure AI Foundry
- GPT models
- LangChain
- LangGraph
Related services
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