Kurt Shintaku's Blog

Retrieval-Augmented Generation (RAG) is a commonly used design pattern that provides a human language interface using a “Large Language Model” (i.e. ChatGPT) against a distinct & separate data source (i.e. search index, database table, etc.) to generate a response based on your specific data. The power of Azure OpenAI models in this RAG pattern gives you more control over the data used by the LLM’s response.

Many organizations have large repositories of document images (FAXs, document scans, photos) that can be reborn as rich data sources for RAG patterns and AI models and can enable conversational exchanges that respond based on that unlocked image data.

We’ve provided documentation around how to implement a RAG model that specifically leverages “Azure AI Document Intelligence”, it’s Optical Character Recognition and its unique “Document Layout Model” to understand the document context without training. The pattern can continuously take advantage of your repositories of scanned images & documents to provide more relevant responses.

Read about the pattern here:

• Retrieval-Augmented Generation with Azure AI Document Intelligence
  https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/concept-retrieval-augmented-generation?view=doc-intel-4.0.0