| 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. |