RAG systems may seem simple on the surface, but their effectiveness relies on the processes behind the scenes. This guide examines how Azure AI Document Intelligence addresses the challenges of data processing in RAG pipelines. By introducing advanced features such as semantic chunking and extracting data from complex formats like images and tables, it redefines how information is prepared and retrieved. Backed by real-world testing, we’ll highlight the tangible improvements these innovations bring to the accuracy and relevance of LLM-generated responses.
Insights_Insight_Guide_How does Azure AI Document Intelligence boost RAG performance?
Here’s what you’ll take away:
- Semantic Chunking in Action: Understand how meaning-based text segmentation enhances the relevance and coherence of information retrieval.
- Enhanced Data Scope: Learn how Azure AI Document Intelligence extracts meaningful data from tables, images, and structured documents.
- Real-World Performance Evaluation: Discover how semantic chunking led to an 8% improvement in precision, recall, and F1 scores during a study using 122 documents and 30 questions.
- Actionable Insights: Practical steps to implement Azure AI Document Intelligence into your own RAG pipeline for measurable impact.
