Update and rename Begineerguidedocprocessing.md to begineer_guide_doc_processing.md

Author: Additi

articles/ai-services/content-understanding/Begineerguidedocprocessing.md renamed to articles/ai-services/content-understanding/begineer_guide_doc_processing.md

@@ -1,12 +1,26 @@
+---
+
+title: Begineer guide for document processing
+titleSuffix: Azure AI services
+description: Learn about Azure AI Content Understanding, Azure AI Document Intelligence and Azure LLM solutions, processes, workflows, use-cases, and field extractions for document processing.
+author: laujan
+ms.author: admaheshwari
+manager: nitinme
+ms.date: 06/26/2025
+ms.service: azure-ai-content-understanding
+ms.topic: overview
+---
+ 
+
 # Beginner’s Guide: Choosing Between Azure Document Intelligence, Azure AI Content Understanding, and Azure OpenAI for Document Processing
 
-As Generative AI becomes the standard approach for processing documents and unstructured content, organizations are faced with a variety of choices on how best to build their document processing pipelines. While OCR-based tools served well for traditional forms and invoices, modern workflows increasingly involve multimodal content — documents, images, emails, audio recordings, and even videos.
+As Generative AI becomes the go to approach for processing documents and unstructured content, organizations are faced with a variety of choices on how to build their document processing pipelines more robust, secure and scalable. While OCR-based services served well for traditional forms, modern workflows increasingly involve multimodal content — documents, images, audio recordings, text and videos.
 
-Azure AI Document Intelligence remains the trusted and proven option for many document-centric scenarios. Customers continue to rely on it for high-accuracy extraction from structured or semi-structured documents such as invoices, purchase orders, receipts, tax forms, and identification cards. It also remains a popular choice as a preprocessing step, where documents are digitized and structured before being passed to downstream Gen AI models for reasoning or summarization.
+Azure AI Document Intelligence remains the trusted and proven option for many document-centric scenarios. Customers continue to rely on it for high-accuracy extraction from structured, unstructured or semi-structured documents such as invoices, purchase orders, receipts, tax forms, and identification cards. It also remains a popular choice as a preprocessing step, where documents are digitized and structured for processing via downstream Gen AI models for reasoning or summarization.
 
-Azure AI Content Understanding is a newer, purpose-built service that addresses today’s enterprise challenges in processing multimodal, mixed-format, and context-rich content. It combines content extraction with built-in reasoning, enrichment, validation, and decision-making capabilities — removing the need for custom orchestration or multiple point services. CU is designed for end-to-end multimodal processing, handling not just documents but images, audio, video, and diverse file formats in unified workflows.
+Azure AI Content Understanding is the latest preview, purpose-built service that addresses today’s enterprise challenges in processing multimodal, mixed-format, and context-rich content. It combines content extraction with built-in reasoning, enrichment, validation, and decision-making capabilities — removing the need for custom orchestration or multiple point services. CU is designed for end-to-end multimodal processing, handling not just documents but images, audio, video, and diverse file formats in unified workflows with zero-shot capabilities. 
 
-For organizations requiring niche AI workflows or operating on the cutting edge, custom solutions built with Azure OpenAI Service offer maximum flexibility. Developers can combine models like GPT-4o, Vision, Whisper, and Embeddings to build highly customized AI solutions, typically integrating Document Intelligence/ Content Understanding for extraction and wrapping AI reasoning models with tailored prompts, APIs, and business logic.
+For organizations requiring niche AI workflows or operating on the cutting edge, custom solutions built with Azure OpenAI Service/ or any other Azure based LLM services offer maximum flexibility. Developers can combine models like GPT-4o, Vision, Whisper, and Embeddings to build highly customized AI solutions, typically integrating Azure Document Intelligence/ Azure AI Content Understanding for extraction and wrapping AI reasoning models with tailored prompts, APIs, and business logic.
 
 This document will help you compare and contrast the experience, capabilities, integration patterns, operational complexity of these three approaches — providing clear guidance on when to choose each, and how they complement one another in real-world enterprise content processing scenarios.
 
@@ -18,72 +32,72 @@ Here’s a summary of the three available services:
 
 | Service | What it Does | Ideal For | Strengths | Core Features |
 |--------|---------------|-----------|-----------|----------------|
-| Azure AI Document Intelligence (DI) | Extracts text, key-value pairs, tables, and layout from structured documents | Standard forms, invoices, receipts, purchase orders, IDs | Proven, high-accuracy extraction with prebuilt and custom models | OCR/Read/Layout models, Prebuilt Models (invoice, tax, receipt, etc), Custom model (extraction and classification) |
-| Azure AI Content Understanding (CU) | Processes documents, images, audio, and video; performs reasoning, validation, enrichment, and decision-making | Complex, multimodal workflows or multi-document processes | Built-in multimodal reasoning and enterprise-grade enrichment | Support for extractive, generative and classification for documents, image, audio, video |
+| Azure AI Document Intelligence (DI) | Extracts text, key-value pairs, tables, and layout from structured, semi and unstructured documents | Standard forms, invoices, receipts, purchase orders, IDs, contracts, legal documents | Proven, high-accuracy extraction with layout, prebuilts and custom models | OCR/Read/Layout models, Prebuilt Models (invoice, tax, receipt, etc), Custom model (extraction and classification) |
+| Azure AI Content Understanding (CU) | Processes documents, images, audio, and video; performs reasoning, validation, enrichment, and decision-making | Complex, multimodal workflows or multi-document processes | Built-in multimodal reasoning and enterprise-grade enrichment, Zero Shot model | Support for extractive, generative and classification for documents, image, audio, video |
 | DIY with Azure OpenAI Service | Fully customizable AI workflows using GPT, Vision, Whisper, and Embeddings | Experimental AI workflows, tailored interactive solutions, or niche reasoning tasks | Maximum flexibility and control | Multiple options to plug and play |
 
 ---
 
 ## Guided Scenario Walkthrough
 
-Let's take a look at various categories of document processing scenarios enterprises face and how to navigate each of such scenarios with the best fitted service.
+Let's take a look at various categories of document processing scenarios that you may encounter and how to navigate each of such scenarios with the best fitted service.
 
 ### Scenario 1: Processing a Standardized, Single-Format Form
 
 **Business Process**:  
-Extract fixed fields like Name, Date of Birth, Address, Account Number, and Signature from forms with identical layouts every time.  
-**Examples**:
+Extract fixed fields like Name, Date of Birth, Address, Account Number, and other details from forms with identical templates every time.  **Examples**:
 - Employment onboarding form (same layout for all employees)
 - Fixed-format tax forms (W-2, 1099)
 - Airline refund request form
 - Bank account opening application
 
 **Decision Path**:
-- **Azure AI Document Intelligence**: Can use prebuilt models if available (like ID or receipt) or train a custom model with 5–10 samples via Document Studio or use layout to extract all the content.
-- **Azure AI Content Understanding**: Can do the same with CU, No additional value over DI.
-- **DIY with OpenAI**: Inefficient and costly for simple structured forms.
+- **Azure AI Document Intelligence**: You can choose to use layout model for RAG,  prebuilt models if available (like ID or receipt) or train a custom model with 5–10 samples via Document Studio.
+- **Azure AI Content Understanding**: You can choose to use content understanding and defining the schema to get zero shot results. 
+- **DIY with OpenAI**: Tailored effort with DIY for simple structured forms.
+
+**Recommended**:
+-DI for handling the form extraction at scale. 
 
 ---
 
 ### Scenario 2: Managing Document with Few Known Variants
 
 **Business Process**:  
-Extract consistent fields (name, amount, policy number, claim date) across a small, known set of layouts.  
-**Examples**:
+Extract consistent fields (name, amount, policy number, claim date) across a small, known set of templates.  **Examples**:
 - Insurance claim forms with 3 formats (Eg: US, UK, APAC)
 - Annual tax forms with minor layout updates each year
 - University admission applications for different degree programs
 - Employee expense reports with department-specific templates
 
 **Decision Path**:
-- **Azure AI Document Intelligence**: Train custom models with at least 5 samples of each variant and combine variants into a single model if differences are minor or train a separate model for each variant and use a classifier to route documents to the right model.
-- **Azure AI Content Understanding**: Ideal if variants change frequently or labeled samples are unavailable. CU uses zero-shot extraction with a defined schema and AI inference to find fields across variants.
-- **DIY with OpenAI**: Adds additional development effort to handle consistency.
+- **Azure AI Document Intelligence**: Train custom models with at least 5 samples of each variant and combine variants into a single model if differences are minor or train a separate model for each variant and use a classifier to route documents to the right model. You can also use any existing prebuilt model (like US tax forms, invoice , receipts) for extraction. 
+- **Azure AI Content Understanding**: CU uses zero-shot extraction with a defined schema and infers to find fields across variants. 
+- **DIY with OpenAI**: Additional development effort to handle consistency.
 
 **Recommended**:
-- DI if variants are stable and sample sets are manageable
-- CU if variants are unpredictable or labels are hard to acquire
+- DI if variants are stable and sample sets are manageable.
+- CU if variants are unpredictable or labels are hard to acquire.
 
 ---
 
 ### Scenario 3: High-Variation Semi-Structured Documents
 
 **Business Process**:  
-Extract key fields like Invoice Number, Vendor Name, Total Amount, Line Items, and Dates from highly varied documents with inconsistent templates.  
-**Examples**:
+Extract key fields like Invoice Number, Vendor Name, Total Amount, Line Items, and Dates from highly varied documents with inconsistent templates.  **Examples**:
 - Invoices from multiple vendors all with different formats
 - Receipts from international store chains
 - Delivery notes with different templates from vendors
 - Purchase orders with inconsistent layouts across suppliers
 - Student transcripts from different universities
 
 **Decision Path**:
-- **Azure AI Document Intelligence**: Use the prebuilt Invoice model for fields it supports. If custom fields are needed, train a custom model, however with high variation, labelling at scale is challenging and will require hundreds of labeled documents.
+- **Azure AI Document Intelligence**: Use the prebuilt Invoice model for fields it supports. If custom fields are needed, train a custom model, however with high variation, labelling at scale is challenging and will require large number of of labeled documents.
 - **Azure AI Content Understanding**: Excels at handling multi-language, multi-layout documents without labelling. CU uses contextual inference (e.g., recognizing “Invoice Ref” or “Reference No.” as the same field). It is also capable of reasoning across multiple documents (matching a PO to its invoice).
 - **DIY with OpenAI**: Requires OCR processing, prompt chaining, and orchestration logic for multi-doc reasoning. Need to scale the pipeline and address enterprise grade features for production.
 
 **Recommended**:
-- DI prebuilt if required fields match the model output, else custom model with labelling.
+- DI prebuilt if required fields match the model output, else use custom model with labelling.
 - CU for diverse layouts, multi-language support, and logic-heavy validation as it requires no labelling and you can fine tune by adding 1-2 examples of edge cases.
 - DIY only for highly custom or interactive solutions
 
@@ -92,8 +106,7 @@ Extract key fields like Invoice Number, Vendor Name, Total Amount, Line Items, a
 ### Scenario 4: Extracting Insights from Unstructured Documents
 
 **Business Process**:  
-Extract abstract concepts like obligations, contract parties, risk indicators, sentiment, or decisions from free-text, multi-page, narrative documents.  
-**Examples**:
+Extract, generate abstract details like obligations, summaries, inferencing details like contract parties, risk indicators, sentiment, or decisions from free-text, multi-page, narrative documents.  **Examples**:
 - Legal contracts and service agreements
 - Investment reports
 - Research papers
@@ -113,33 +126,19 @@ Extract abstract concepts like obligations, contract parties, risk indicators, s
 
 ### Scenario 5: Multi-Document, Mixed Media Processing
 
-**Examples of document sets**:
-- Onboarding kits: PDF forms + ID images + recorded video interviews
+**Business Process**:  
+Aggregate content from diverse formats, cross-reference details, validate consistency (e.g., name matches across documents), and surface inconsistencies. **Examples**:
+- Onboarding content: PDF forms + ID images + recorded video interviews
 - Compliance cases: Email text + contract + call transcript
 - Medical claims: Doctor notes + lab reports + phone consultations
 - Multimedia RFP submissions: Proposal PDF + product images + explainer videos
 
-**Business Process**:  
-Aggregate content from diverse formats, cross-reference details, validate consistency (e.g., name matches across documents), and surface inconsistencies.
-
 **Decision Path**:
-- **Azure AI Document Intelligence**: Only handles forms and scanned documents. Cannot process audio or video.
-- **Azure AI Content Understanding**: Purpose-built for this. It can process text, images, audio, and video simultaneously, cross-check data across them, and enrich outputs with face recognition, transcription, and video chaptering.
+- **Azure AI Document Intelligence**: Only handles forms and scanned documents. Cannot process audio or video. Need to use other services for other modalities. 
+- **Azure AI Content Understanding**:Ideal for handling text, images, audio, and video simultaneously, cross-check data across them, and enrich outputs with face recognition, transcription, and video chaptering.
 - **DIY with OpenAI**: Technically feasible but requires stitching together DI for OCR, Whisper for audio, Vision for images, and GPT for reasoning — with complex orchestration and maintenance.
 
-**Recommended**: Azure AI Content Understanding
-
----
-
-## Extra Examples for Each Document Type
-
-| Category | Example Document Types |
-|----------|------------------------|
-| Standardized Forms | HR onboarding forms, account opening forms, fixed-format receipts, fixed custom formats |
-| Few Known Variants | Tax forms by year, mortgage forms, ID documents across nations |
-| High-Variant Semi-Structured | Vendor invoices, supplier POs, medical records, enterprise tax forms |
-| Unstructured Text Documents | Contracts, legal notices, policies, survey feedback |
-| Multimodal Content Sets | Loan processing, Compliance checks, Call center scenarios |
+**Recommended**: Azure AI Content Understanding for simple one-stop solution
 
 ---
 
@@ -150,7 +149,7 @@ Aggregate content from diverse formats, cross-reference details, validate consis
 | Unified, multimodal pipeline | ✅ Supports docs, images, audio, video | ❌ Requires orchestration |
 | Enterprise reasoning workflows | ✅ In-built reasoning capabilities | ❌ Custom chaining |
 | Prebuilt enrichments and schema normalization | ✅ Prebuilt templates available | ❌ Requires implementation |
-| Simplified pricing | ✅ Token based pricing | ❌ Token-based, variable |
+| Simplified pricing | ✅ Token based pricing |  ✅ Token based pricing |
 | Enterprise governance & security | ✅ Azure security compliance | ❌ Custom implementation |
 | Confidence and Grounding | ✅ In-built scores | ❌ Custom implementation |
 | Chunking & normalization | ✅ Built-in algorithms | ❌ Custom implementation |
@@ -177,4 +176,4 @@ Choosing the right document processing service depends on your document complexi
 - Move to **Azure AI Content Understanding** for reasoning, multi-format content, or complex business logic.
 - Leverage **Azure OpenAI Service** for custom, experimental, or conversational AI workflows where managed services aren’t a fit.
 
-Many enterprises combine these services into hybrid pipelines — using Document Intelligence/ CU for extraction and CU or OpenAI for enrichment and reasoning.
+Many enterprises combine these services into hybrid pipelines — using Document Intelligence/ Content Understanding for extraction and CU or OpenAI for enrichment and reasoning.