Expert knowledge for Azure AI Document Intelligence development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using AnalyzeDocument v4.0, custom models, containers/Docker, SAS/managed identity auth, or SDK/REST workflows, and other Azure AI Document Intelligence related development tasks. Not for Azure AI Vision (use azure-ai-vision), Azure AI Custom Vision (use azure-custom-vision), Azure AI Search (use azure-cognitive-search), Azure AI Video Indexer (use azure-video-indexer).
v1.0Latest
New~2.7kUpdated Jun 26, 2026
Azure AI Document Intelligence Skill
This skill provides expert guidance for Azure AI Document Intelligence. Covers troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. It combines local quick-reference content with remote documentation fetching capabilities.
How to Use This Skill
IMPORTANT for Agent: Use the Category Index below to locate relevant sections. For categories with line ranges (e.g., L35-L120), use read_file with the specified lines. For categories with file links (e.g., [security.md](security.md)), use read_file on the linked reference file
IMPORTANT for Agent: If metadata.generated_at is more than 3 months old, suggest the user pull the latest version from the repository. If mcp_microsoftdocs tools are not available, suggest the user install it: Installation Guide
This skill requires network access to fetch documentation content:
Preferred: Use mcp_microsoftdocs:microsoft_docs_fetch with query string from=learn-agent-skill. Returns Markdown.
Fallback: Use fetch_webpage with query string from=learn-agent-skill&accept=text/markdown. Returns Markdown.
Category Index
Category
Lines
Description
Troubleshooting
L37-L43
Diagnosing and fixing Document Intelligence issues: latency/performance problems, service error codes and meanings, and known Foundry-specific bugs and workarounds.
Best Practices
L44-L54
Improving custom model accuracy and confidence, labeling and table-tagging best practices, training/classification workflows, and managing the full Document Intelligence model lifecycle
Decision Making
L55-L60
Guidance on choosing the right Document Intelligence model for your scenario and planning/migrating workloads to the v4.0 API and feature set.
Architecture & Design Patterns
L61-L65
Guidance on designing disaster recovery, redundancy, and failover strategies for Azure AI Document Intelligence models and deployments.
Limits & Quotas
L66-L75
Quotas, capacity add-ons, throttling behavior, batch scaling, and language/OCR support limits for Document Intelligence (service, custom, and prebuilt models).
Security
L76-L83
Securing Document Intelligence: creating SAS tokens, configuring data-at-rest encryption, and using managed identities and VNets to lock down access to resources.
Configuration
L84-L89
Configuring Document Intelligence containers and building, training, and composing custom models for tailored document processing workflows.
Integrations & Coding Patterns
L90-L99
Using SDKs/REST to call Document Intelligence, handle AnalyzeDocument/Markdown outputs, and integrate with apps, Azure Functions, and Logic Apps for end‑to‑end document workflows
Deployment
L100-L106
Deploying Document Intelligence via Docker/containers, including image tags, offline/disconnected setups, and installing/running the service and sample labeling tool.
This skill provides expert reference guidance for Azure AI Document Intelligence development, covering troubleshooting, best practices, architecture, security, configuration, integrations, and deployment. It uses a category-indexed structure to guide agents to relevant documentation sections, supplemented with remote documentation fetching via `mcp_microsoftdocs` or standard fetch endpoints. No code execution, file writes, or system modifications are performed.
Phrases that MCP clients use to match this skill to user intent.
document intelligence troubleshootingcustom model trainingdocument processing designcontainer deploymentazure document intelligencemodel migration guidanceocr and language support
Referenced Domains
External domains referenced in skill content, detected by static analysis.
github.comlearn.microsoft.com
Use Cases
Troubleshoot Document Intelligence latency and performance issues
Improve accuracy of custom document classification models
Choose appropriate Document Intelligence models for use cases
Design disaster recovery and failover strategies
Configure SAS tokens, encryption, and managed identity authentication
Build and train custom document processing models
Integrate Document Intelligence with Azure Functions and Logic Apps
Deploy Document Intelligence containers in disconnected environments
Understand service limits, quotas, and throttling behavior
Quality Notes
Well-structured category index with line ranges and clear descriptions enabling efficient agent navigation
Comprehensive coverage of nine major topic areas with curated Microsoft Learn links
Clear instructions for agents on when/how to use local sections vs. remote documentation fetching
Helpful guidance on tool availability (mcp_microsoftdocs preferred, fetch_webpage fallback) with installation links
All linked Microsoft Learn URLs are current and match declared scope (Document Intelligence v4.0)
No supporting reference files (security.md, etc.) are included despite being referenced in instructions — agents must rely on remote fetching
Minor clarity issue: instructions reference 'file links' like [security.md] but no such files exist in manifest
Model: claude-haiku-4-5-20251001Analyzed: Jun 26, 2026
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