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MicrosoftDocs/azure-language-service

MicrosoftDocs

azure-language-service

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 using CLU, custom NER/classification, CQA, sentiment/summarization, or PII/health text analysis APIs, 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 Bot Service (use azure-bot-service).

v1.0Latest
New~5.4kUpdated Jun 26, 2026

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

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 custom text classification and custom question answering, including model performance, configuration, and runtime/response problems.
Best Practices L43-L59 Best practices for designing, labeling, and evaluating CLU, custom NER, text classification, and CQA projects, including multilingual handling, emojis, schemas, and autolabeling.
Decision Making L60-L68 Guidance on choosing regions and resources, lifecycle policies, and migration paths from LUIS, QnA Maker, Text Analytics, and Language Studio to Azure Language and Microsoft Foundry
Architecture & Design Patterns L69-L75 Architectural guidance for CLU and custom text classification: choosing CLU vs orchestration workflows, and designing regional backup, redundancy, and failover strategies.
Limits & Quotas L76-L95 Limits, quotas, and language/region support for Azure AI Language features (CLU, NER, PII, QnA, etc.), including data sizes, throughput, containers, and model lifecycles.
Security L96-L106 Security, encryption, and access control for Azure AI Language: RBAC, managed identities, SAS, CMK/data-at-rest, network isolation, Private Link, and CQA-specific security setup.
Configuration L107-L131 Configuring Azure AI Language/CLU/NER/CQA projects and containers, including data formats, resources, Docker/on-prem setups, metrics, confidence scores, PII redaction, and sentiment/summarization.
Integrations & Coding Patterns L132-L163 Implementing Azure AI Language features via REST/SDKs: CLU, custom NER/classification, CQA, sentiment, summarization, health, entity linking, and integrating with bots/Power Automate.
Deployment L164-L173 How to deploy and run Azure AI Language models (custom classification, NER, QnA, key phrases, language detection) across regions, containers, AKS, and migrate projects/resources.

Troubleshooting

Topic URL
Resolve common issues in custom text classification https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-text-classification/faq
Diagnose and resolve custom question answering issues https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/how-to/troubleshooting

Best Practices

Topic URL
Handle multilingual and emoji offsets in Language https://learn.microsoft.com/en-us/azure/ai-services/language-service/concepts/multilingual-emoji-support
Apply CLU conversational design best practices https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/concepts/best-practices
Implement multilingual CLU projects effectively https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/concepts/multiple-languages
Design effective CLU project schemas https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/how-to/build-schema
Tag and label utterances for CLU training https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/how-to/tag-utterances
Interpret and stabilize CLU model evaluations https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/how-to/view-model-evaluation
Prepare data and design schemas for custom NER https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-named-entity-recognition/how-to/design-schema
Label data effectively for custom NER training https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-named-entity-recognition/how-to/tag-data
Use autolabeling to accelerate custom NER annotation https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-named-entity-recognition/how-to/use-autolabeling
Prepare data and design schemas for text classification https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-text-classification/how-to/design-schema
Label data effectively for custom text classification https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-text-classification/how-to/tag-data
Implement best practices for CQA project quality https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/concepts/best-practices
Apply project authoring best practices in CQA https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/how-to/best-practices

Decision Making

Topic URL
Choose Azure regions for Language service features https://learn.microsoft.com/en-us/azure/ai-services/language-service/concepts/regional-support
Migrate Azure Language Studio projects to Microsoft Foundry https://learn.microsoft.com/en-us/azure/ai-services/language-service/migration-studio-to-foundry
Choose and manage Azure resources for CQA https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/concepts/azure-resources
Decide migration from LUIS and QnA Maker to Azure Language https://learn.microsoft.com/en-us/azure/ai-services/language-service/reference/migrate
Migrate Text Analytics apps to Azure Language API https://learn.microsoft.com/en-us/azure/ai-services/language-service/reference/migrate-language-service-latest

Architecture & Design Patterns

Topic URL
Choose CLU vs orchestration workflow architecture https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/concepts/app-architecture
Design CLU regional backup and failover https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/how-to/fail-over
Design regional fail-over for custom text classification solutions https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-text-classification/fail-over

Limits & Quotas

Topic URL
Data size and rate limits for Azure Language features https://learn.microsoft.com/en-us/azure/ai-services/language-service/concepts/data-limits
Understand lifecycle timelines for Azure Language models https://learn.microsoft.com/en-us/azure/ai-services/language-service/concepts/model-lifecycle
Train and manage CLU model jobs and limits https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/how-to/train-model
Apply CLU Docker container request limits https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/how-to/use-containers
Apply CLU data, region, and throughput limits https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/service-limits
Check language and region support for custom NER https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-named-entity-recognition/language-support
Language support matrix for custom text classification https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-text-classification/language-support
Review custom text classification data and rate limits https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-text-classification/service-limits
Check language support for entity linking API https://learn.microsoft.com/en-us/azure/ai-services/language-service/entity-linking/language-support
Check language support for key phrase extraction https://learn.microsoft.com/en-us/azure/ai-services/language-service/key-phrase-extraction/language-support
Review language detection supported languages and codes https://learn.microsoft.com/en-us/azure/ai-services/language-service/language-detection/language-support
Review language support for Named Entity Recognition https://learn.microsoft.com/en-us/azure/ai-services/language-service/named-entity-recognition/language-support
Review orchestration workflow data and throughput limits https://learn.microsoft.com/en-us/azure/ai-services/language-service/orchestration-workflow/service-limits
Apply PII container per-call character and document limits https://learn.microsoft.com/en-us/azure/ai-services/language-service/personally-identifiable-information/how-to/use-containers
Check language support for Azure PII detection https://learn.microsoft.com/en-us/azure/ai-services/language-service/personally-identifiable-information/language-support
Custom question answering limits and boundaries https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/concepts/limits

Security

Topic URL
Understand Language service data-at-rest encryption https://learn.microsoft.com/en-us/azure/ai-services/language-service/concepts/encryption-data-at-rest
Apply Azure RBAC to Azure Language resources https://learn.microsoft.com/en-us/azure/ai-services/language-service/concepts/role-based-access-control
Use managed identities for Language Blob access https://learn.microsoft.com/en-us/azure/ai-services/language-service/native-document-support/managed-identities
Create SAS tokens for Language Blob access https://learn.microsoft.com/en-us/azure/ai-services/language-service/native-document-support/shared-access-signatures
Configure Azure resources and permissions for CQA fine-tuning https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/how-to/configure-azure-resources
Configure data-at-rest encryption and CMK for CQA https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/how-to/encrypt-data-at-rest
Configure network isolation and Private Link for CQA https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/how-to/network-isolation

Configuration

Topic URL
Configure Azure resources for CLU fine-tune models https://learn.microsoft.com/en-us/azure/ai-services/language-service/concepts/configure-azure-resources
Configure Azure Language service containers https://learn.microsoft.com/en-us/azure/ai-services/language-service/concepts/configure-containers
Format data correctly for CLU projects https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/concepts/data-formats
Configure and use CLU None intent https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/concepts/none-intent
Use CLU prebuilt entity components https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/prebuilt-component-reference
Create custom NER projects and configure Azure resources https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-named-entity-recognition/how-to/create-project
Configure and run Custom NER Docker containers on-premises https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-named-entity-recognition/how-to/use-containers
Use required data formats for custom text classification https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-text-classification/concepts/data-formats
Configure and run training jobs for text classification models https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-text-classification/how-to/train-model
View and interpret evaluation metrics for text classification models https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-text-classification/how-to/view-model-evaluation
Map NER entity types and tags across API versions https://learn.microsoft.com/en-us/azure/ai-services/language-service/named-entity-recognition/concepts/ga-preview-mapping
Configure NER skill parameters and inference options https://learn.microsoft.com/en-us/azure/ai-services/language-service/named-entity-recognition/how-to/skill-parameters
Configure native document PII redaction with Azure Language https://learn.microsoft.com/en-us/azure/ai-services/language-service/personally-identifiable-information/how-to/redact-document-pii
Understand and configure confidence scores in CQA https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/concepts/confidence-score
Enable diagnostics and run analytics for CQA projects https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/how-to/analytics
Add and configure chitchat personas in CQA https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/how-to/chit-chat
Use supported markdown formats in CQA answers https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/reference/markdown-format
Run Sentiment Analysis Docker containers https://learn.microsoft.com/en-us/azure/ai-services/language-service/sentiment-opinion-mining/how-to/use-containers
Run Summarization Docker containers on-premises https://learn.microsoft.com/en-us/azure/ai-services/language-service/summarization/how-to/use-containers
Configure Text Analytics for health containers https://learn.microsoft.com/en-us/azure/ai-services/language-service/text-analytics-for-health/how-to/configure-containers
Run Text Analytics for health containers https://learn.microsoft.com/en-us/azure/ai-services/language-service/text-analytics-for-health/how-to/use-containers

Integrations & Coding Patterns

Topic URL
Integrate Azure Language SDK and REST APIs https://learn.microsoft.com/en-us/azure/ai-services/language-service/concepts/developer-guide
Use Azure Language features asynchronously https://learn.microsoft.com/en-us/azure/ai-services/language-service/concepts/use-asynchronously
Call CLU prediction APIs and SDKs https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/how-to/call-api
Create CLU fine-tuning tasks via Foundry and REST API https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/how-to/create-project
Integrate CLU with Bot Framework SDK https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/tutorials/bot-framework
Start building custom NER models via Foundry or REST https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-named-entity-recognition/quickstart
Send prediction requests to custom text classification deployments https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-text-classification/how-to/call-api
Call the entity linking API with correct parameters https://learn.microsoft.com/en-us/azure/ai-services/language-service/entity-linking/how-to/call-api
Call entity linking via SDKs and REST API https://learn.microsoft.com/en-us/azure/ai-services/language-service/entity-linking/quickstart
Call the key phrase extraction API correctly https://learn.microsoft.com/en-us/azure/ai-services/language-service/key-phrase-extraction/how-to/call-api
Use key phrase extraction via .NET client library https://learn.microsoft.com/en-us/azure/ai-services/language-service/key-phrase-extraction/quickstart
Call language detection API and interpret results https://learn.microsoft.com/en-us/azure/ai-services/language-service/language-detection/how-to/call-api
Implement language detection using SDKs and REST https://learn.microsoft.com/en-us/azure/ai-services/language-service/language-detection/quickstart
Call the NER API to extract named entities https://learn.microsoft.com/en-us/azure/ai-services/language-service/named-entity-recognition/how-to-call
Use the NER client library to extract entities https://learn.microsoft.com/en-us/azure/ai-services/language-service/named-entity-recognition/quickstart
Use Authoring API for custom question answering automation https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/how-to/authoring
Call the prebuilt CQA API for ad-hoc answering https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/how-to/prebuilt
Call Sentiment and Opinion Mining APIs https://learn.microsoft.com/en-us/azure/ai-services/language-service/sentiment-opinion-mining/how-to/call-api
Call Sentiment Analysis via SDK and REST https://learn.microsoft.com/en-us/azure/ai-services/language-service/sentiment-opinion-mining/quickstart
Call conversation summarization API for chats https://learn.microsoft.com/en-us/azure/ai-services/language-service/summarization/how-to/conversation-summarization
Summarize native documents via API https://learn.microsoft.com/en-us/azure/ai-services/language-service/summarization/how-to/document-summarization
Use extractive text summarization API https://learn.microsoft.com/en-us/azure/ai-services/language-service/summarization/how-to/text-summarization
Use Azure Summarization via SDK and REST https://learn.microsoft.com/en-us/azure/ai-services/language-service/summarization/quickstart
Enable FHIR structuring in health API output https://learn.microsoft.com/en-us/azure/ai-services/language-service/text-analytics-for-health/concepts/fhir
Interpret relation extraction JSON output https://learn.microsoft.com/en-us/azure/ai-services/language-service/text-analytics-for-health/concepts/relation-extraction
Call Text Analytics for health API https://learn.microsoft.com/en-us/azure/ai-services/language-service/text-analytics-for-health/how-to/call-api
Call Text Analytics for Health via REST and SDKs https://learn.microsoft.com/en-us/azure/ai-services/language-service/text-analytics-for-health/quickstart
Use Azure Language in Power Automate flows https://learn.microsoft.com/en-us/azure/ai-services/language-service/tutorials/power-automate

Deployment

Topic URL
Deploy custom language projects to multiple regions https://learn.microsoft.com/en-us/azure/ai-services/language-service/concepts/custom-features/multi-region-deployment
Deploy custom text classification models for prediction https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-text-classification/how-to/deploy-model
Run key phrase extraction in Docker containers on-premises https://learn.microsoft.com/en-us/azure/ai-services/language-service/key-phrase-extraction/how-to/use-containers
Deploy language detection with Docker containers on-premises https://learn.microsoft.com/en-us/azure/ai-services/language-service/language-detection/how-to/use-containers
Deploy NER with Docker containers on-premises https://learn.microsoft.com/en-us/azure/ai-services/language-service/named-entity-recognition/how-to/use-containers
Move custom question answering projects between resources https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/how-to/migrate-knowledge-base
Deploy Language containers to Azure Kubernetes Service https://learn.microsoft.com/en-us/azure/ai-services/language-service/tutorials/use-kubernetes-service
Files1
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Overall Score

82/100

Grade

B

Good

Safety

85

Quality

83

Clarity

88

Completeness

78

Summary

This skill provides expert guidance for Azure AI Language service development, covering troubleshooting, best practices, decision-making, architecture patterns, limits/quotas, security, configuration, integrations, and deployment. It combines a detailed local category index with remote documentation fetching via Microsoft Docs, enabling agents to reference Azure Language documentation at runtime.

Detected Capabilities

documentation lookupnetwork requests (microsoft docs fetch)markdown content retrievalreference index navigationtool availability detection

Trigger Keywords

Phrases that MCP clients use to match this skill to user intent.

azure language serviceconversational language understandingcustom named entity recognitioncustom text classificationquestion answeringsentiment analysisazure ai language troubleshootinglanguage detectionentity linkingtext analytics

Risk Signals

INFO

Requires network access to fetch remote documentation via mcp_microsoftdocs or fetch_webpage

Compatibility section and 'How to Use' instructions
INFO

Directs agent to fall back to fetch_webpage with query string if mcp_microsoftdocs unavailable

How to Use section, fallback instructions
INFO

Referenced domains (github.com, learn.microsoft.com) are first-party Microsoft documentation sources

Category index URLs

Referenced Domains

External domains referenced in skill content, detected by static analysis.

github.comlearn.microsoft.com

Use Cases

  • Troubleshooting Azure AI Language features like CLU, custom NER/classification, and question answering
  • Designing CLU and text classification projects with best practices for labeling and evaluation
  • Choosing between Azure regions, resources, and migration paths from legacy services (LUIS, QnA Maker, Text Analytics)
  • Implementing architectural patterns for CLU including failover and multi-region deployment
  • Understanding limits, quotas, and language support across Azure AI Language APIs
  • Configuring security (RBAC, encryption, network isolation) for Language resources and projects
  • Building integrations with Azure Language APIs via REST and SDKs (custom NER, classification, CQA, sentiment, health)
  • Deploying language models to Docker containers, AKS, and multiple regions

Quality Notes

  • Well-structured category index with clear line ranges and descriptive titles makes content navigation explicit
  • Comprehensive URL mappings for all eight category sections with relevant learn.microsoft.com documentation links
  • Clear prerequisites documented: requires network access and optional mcp_microsoftdocs tool
  • Fallback instructions provided if preferred MCP tool unavailable, with query parameters for markdown format
  • Metadata includes generation timestamp (3-month staleness check recommended)
  • Skill scope is clearly bounded to Azure Language only, with explicit exclusions for Azure Search, Speech, Translator, and Bot Service
  • Instructions include practical guidance for agents on using read_file and documentation fetching tools
  • License (CC BY 4.0) is appropriately included for generated documentation skill
Model: claude-haiku-4-5-20251001Analyzed: Jun 26, 2026

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