Azure Content Understanding Accuracy Improvement Guide - Custom Analyzer + Prompt Engineering

The article discusses how to improve the accuracy of Azure Content Understanding (ACU) through custom analyzers and prompt engineering. ACU is an intelligent document processing tool that leverages large language models (LLMs) for extracting meaningful information from documents.

Key Points:

1. Custom Analyzers:

   • Field Definitions: Custom analyzers allow users to define specific fields such as VendorName and InvoiceItems. The field definitions include properties like type, method of extraction (e.g., direct extraction or generation), and descriptions that guide the LLM.
   • Behavior Configuration: Users can configure settings such as OCR, layout analysis, and confidence scores for better accuracy.

2. Prompt Engineering:

   • Accurate Field Names: Using meaningful names like VendorName instead of generic placeholders helps the model understand what information to extract.
   • Detailed Descriptions: Providing detailed descriptions for fields gives more context to the LLM about expected formats and content, improving extraction accuracy.
   • Few-Shot Prompting: Utilizing a few labeled examples can help the model generalize better and extract information accurately.

3. Workflow:

   • ACU first extracts text and structural information from documents using OCR

Read the full article at Towards AI - Medium

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