Official Technical Resources: Microsoft (page 17)
Official Technical Resources · Microsoft: 312 entries in total; this page lists entries 241–255.
Disclosure design patterns for synthetic voices
Design patterns and best practices for disclosure.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Speech Recognition and Synthesis · Multimodal Models
Link last checked: 2026-09-24
Disclosure design patterns for text to speech avatar
Design patterns and best practices for avatar disclosure.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Speech Recognition and Synthesis · Multimodal Models
Link last checked: 2026-09-24
Data, privacy, and security for text to speech
This document details issues for data, privacy, and security for text to speech in Speech Service.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Speech Recognition and Synthesis · Privacy and Data Compliance · AI Governance and Regulation
Link last checked: 2026-09-24
Disclosure for voice and avatar talent
Disclosure for voice and avatar talent which covers background as well as best patterns and practices for deployment of this technology.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Speech Recognition and Synthesis · Multimodal Models
Link last checked: 2026-09-24
Data, privacy, and security for Azure AI Voice Live API
This document details issues for data, privacy, and security for Voice live.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Speech Recognition and Synthesis · Privacy and Data Compliance · AI Governance and Regulation
Link last checked: 2026-09-24
Custom categories (preview)
Learn about custom content categories and the different ways you can use Azure AI Content Safety to handle them on your platform.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: AI Governance and Regulation · Human-in-the-loop (HITL) · AI Agent
Link last checked: 2026-09-24
Provenance detection overview
Learn what the Azure AI Content Safety Content Provenance Detection API does, what signals it can detect, common use cases, and current limitations.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: AI-generated Content Detection and Watermarking · AI Governance and Regulation
Link last checked: 2026-09-24
Agentic mode for document analysis (preview)
Learn when to use agentic mode in Azure Content Understanding in Foundry Tools for complex document analysis.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Multimodal Models · Structured Outputs · Retrieval-Augmented Generation (RAG)
Link last checked: 2026-09-24
What is a Content Understanding analyzer?
Learn about Azure Content Understanding in Foundry Tools analyzers, how to configure them, and the parameters you can set when creating custom analyzers.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Multimodal Models · Structured Outputs · Retrieval-Augmented Generation (RAG)
Link last checked: 2026-09-24
Best practices for Azure Content Understanding in Foundry Tools
Learn how to best use Azure Content Understanding in Foundry Tools for document, image, video, and audio file content and field extractions.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Multimodal Models · Structured Outputs · Retrieval-Augmented Generation (RAG)
Link last checked: 2026-09-24
Content Understanding classification/segmentation
Learn about Azure Content Understanding in Foundry Tools classifier solutions.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Multimodal Models · Structured Outputs · Retrieval-Augmented Generation (RAG)
Link last checked: 2026-09-24
Prebuilt analyzers in Azure Content Understanding in Foundry Tools
Learn about prebuilt analyzers, base analyzers, RAG analyzers, vertical analyzers, and how to use and customize them in Azure Content Understanding in Foundry Tools.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Multimodal Models · Structured Outputs · Retrieval-Augmented Generation (RAG)
Link last checked: 2026-09-24
Interpret and improve accuracy and confidence scores
Best practices to interpret and improve Azure Document Intelligence in Foundry Tools accuracy scores from train model operations and confidence scores from analysis operations.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Multimodal Models · Structured Outputs · Retrieval-Augmented Generation (RAG)
Link last checked: 2026-09-24
Analyze document API response
Description of the different objects returned as part of the AnalyzeDocument response and how to use the document analysis response in your applications.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Multimodal Models · Structured Outputs · Retrieval-Augmented Generation (RAG)
Link last checked: 2026-09-24
Which model should I choose?
Choose the best Document Intelligence model for your applications and workflows.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Microsoft's own implementation and claims. Recheck preview features and product lifecycle before use.
Related concepts: Multimodal Models · Structured Outputs · Retrieval-Augmented Generation (RAG)
Link last checked: 2026-09-24