/azure-language-service

name: azure-language-service
description: Expert knowledge for Azure AI Language development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when building CLU, custom NER, sentiment, PII redaction, or conversational question answering solutions, and other Azure AI Language related development tasks. Not for Azure AI Search (use azure-cognitive-search), Azure AI Speech (use azure-speech), Azure Translator (use azure-translator), Azure AI Immersive Reader (use azure-immersive-reader).
compatibility: Requires network access. Uses mcp_microsoftdocs:microsoft_docs_fetch or fetch_webpage to retrieve documentation.
metadata:
  generated_at: "2026-07-26"
  generator: "docs2skills/1.0.0"

Azure AI Language Skill

This skill provides expert guidance for Azure AI Language. 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](https://github.com/MicrosoftDocs/mcp/blob/main/README.md)

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-L42 | Diagnosing and fixing common issues in Azure Language custom NER and conversational question answering (CQA), including model errors, configuration problems, and troubleshooting workflows. | | Best Practices | L43-L54 | Best practices for designing and authoring CLU, custom NER, PII, and CQA projects, including data prep, schemas, lifecycles, chitchat personas, and document formatting. | | Decision Making | L55-L64 | Guides for choosing regions and app types, planning CQA solutions, and deciding or executing migrations from LUIS, QnA Maker, Text Analytics, and Language Studio to Azure Language/Fountry. | | Architecture & Design Patterns | L65-L72 | Designing and implementing regional failover and high-availability patterns for CLU, custom NER, custom text classification, and orchestration workflow models in Azure AI Language. | | Limits & Quotas | L73-L96 | Limits, quotas, languages, and supported entities for Azure Language features (CLU, NER, classification, CQA, health), including data size, rate/throughput, training and model lifecycles. | | Security | L97-L108 | Securing Azure AI Language and CQA: encryption at rest (including CMK), RBAC, managed identities, SAS tokens, network isolation/Private Link, and secure deployment/data access configuration. | | Configuration | L109-L129 | Configuring Azure AI Language projects and containers: resources, versioning, NER entities/skills, orchestration intents, CQA behavior/telemetry, health analytics, and storage/security settings. | | Integrations & Coding Patterns | L130-L152 | Using Azure AI Language APIs/SDKs for NER, entity linking, key phrases, sentiment, language detection, health/FHIR, custom Q&A/CLU, PII redaction, async patterns, and Power Automate integration | | Deployment | L153-L165 | Guides for deploying Azure Language services and custom projects (NER, key phrases, sentiment, health, CQA) across regions, Docker/on-prem, and AKS, plus moving CQA between environments. |

Troubleshooting

| Topic | URL | |-------|-----| | Resolve common issues with custom NER in Azure Language | https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-named-entity-recognition/faq | | Troubleshoot common CQA issues and errors | https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/how-to/troubleshooting |

Best Practices

| Topic | URL | |-------|-----| | Apply CLU project authoring best practices | https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/concepts/best-practices | | Prepare data and design schema for custom NER models | https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-named-entity-recognition/how-to/design-schema | | Adapt PII detection to custom domain terminology | https://learn.microsoft.com/en-us/azure/ai-services/language-service/personally-identifiable-information/how-to/adapt-to-domain-pii | | Implement CQA project best practices | https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/concepts/best-practices | | Follow CQA project development lifecycle | https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/concepts/project-development-lifecycle | | Apply authoring best practices to CQA projects | https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/how-to/best-practices | | Add chitchat personas to CQA projects | https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/how-to/chit-chat | | Apply document formatting best practices for custom Q&A | https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/reference/document-f