Official Technical Resources: Microsoft (page 14)
Official Technical Resources · Microsoft: 312 entries in total; this page lists entries 196–210.
Set up human evaluation for your agents (preview)
Learn how to set up human evaluation for your Microsoft Foundry agents, create templates, and analyze results to improve agent performance.
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: Model Evaluation and Benchmarks · Large Language Model (LLM)
Link last checked: 2026-09-24
Log end user feedback (preview)
Learn how to log end user feedback such as thumbs up/down or rating scales from your AI application using OpenTelemetry semantics in Microsoft Foundry.
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: LLM Observability and Tracing · Model Deployment Options
Link last checked: 2026-09-24
Optimize agent prompts by using Prompt Optimizer (preview)
Learn how to use Prompt Optimizer in Microsoft Foundry to automatically improve your agent's system instructions using AI-driven prompt engineering best practices.
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: LLM Observability and Tracing · Model Deployment Options
Link last checked: 2026-09-24
Configure tracing for AI agent frameworks (preview)
Debug issues and monitor AI agent performance in production by configuring OpenTelemetry tracing for LangChain, LangGraph, Semantic Kernel, and OpenAI Agents SDK.
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: LLM Observability and Tracing · Model Deployment Options
Link last checked: 2026-09-24
Review agent interactions with Trace Replay (preview)
Learn how to use Trace Replay in Microsoft Foundry to navigate, filter, and analyze conversation traces from agent runs.
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: LLM Observability and Tracing · Model Deployment Options
Link last checked: 2026-09-24
Set up tracing in Microsoft Foundry
Learn how to set up tracing in Microsoft Foundry to debug AI agent runs and monitor behavior by sending telemetry to Azure Monitor Application Insights with OpenTelemetry.
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: LLM Observability and Tracing · Model Deployment Options
Link last checked: 2026-09-24
Annotate traces with human feedback (preview)
Learn how to annotate traces with thumbs up or thumbs down feedback in Microsoft Foundry to track quality signals directly on production traces.
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: LLM Observability and Tracing · Model Deployment Options
Link last checked: 2026-09-24
Restrict access to sensitive content in Microsoft Foundry traces (preview)
Learn how to protect sensitive generative AI trace data in Microsoft Foundry by routing it to a dedicated table and restricting access with Azure role-based access control.
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: LLM Observability and Tracing · Model Deployment Options
Link last checked: 2026-09-24
Convert agent traces into evaluation datasets (preview)
Learn how to use data generation in Microsoft Foundry to turn production agent traces into evaluation and fine-tuning datasets.
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: Model Evaluation and Benchmarks · Large Language Model (LLM)
Link last checked: 2026-09-24
Abuse monitoring
Learn about the abuse monitoring capabilities of Azure OpenAI
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: LLM Observability and Tracing · Model Deployment Options
Link last checked: 2026-09-24
System message design
Learn how system messages shape Azure OpenAI chat responses, with best practices for control, safety, and consistency.
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: Prompt Engineering · System Prompts and Role Prompting
Link last checked: 2026-09-24
Groundedness detection filter
Learn how to use groundedness detection in Azure OpenAI in Microsoft Foundry Models to validate LLM responses against source documents for RAG scenarios.
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 Guardrails · AI Governance and Regulation
Link last checked: 2026-09-24
Personally identifiable information (PII) filter
Learn about the Personally Identifiable Information (PII) filter for identifying and flagging known personal information in large language model outputs.
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 Guardrails · AI Governance and Regulation
Link last checked: 2026-09-24
Prompt Shields in Microsoft Foundry
Learn how Prompt Shields in Microsoft Foundry detect and block user prompt attacks and document attacks on your model deployments and agents.
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: Prompt Injection · AI Guardrails · AI Governance and Regulation
Link last checked: 2026-09-24
Protected material detection filter
Learn about the Protected Material Detection Filter for identifying and flagging known protected text and code content in large language model outputs.
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 Guardrails · AI Governance and Regulation
Link last checked: 2026-09-24