Official Technical Resources: Microsoft (page 12)
Official Technical Resources · Microsoft: 312 entries in total; this page lists entries 166–180.
General purpose evaluators
Learn about general-purpose evaluators for generative AI, including coherence and fluency evaluators.
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
Retrieval-Augmented Generation (RAG) evaluators
Learn about Retrieval-Augmented Generation evaluators for assessing relevance, groundedness, and response completeness in generative AI systems.
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
Risk and safety evaluators
Learn about risk and safety evaluators for generative AI, including tools for assessing content safety, jailbreak vulnerabilities, and code security risks.
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
Rubric evaluators (preview)
Learn about rubric evaluators for AI agents, which let you define custom scoring criteria with descriptive rubrics for LLM-as-judge evaluation.
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
Textual similarity evaluators
Learn about textual similarity evaluators for generative AI, including semantic similarity, F1 score, BLEU, GLEU, ROUGE, and METEOR metrics.
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
Observability in generative AI
Learn how Microsoft Foundry enables safe, high-quality generative AI through systematic evaluation and observability 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: LLM Observability and Tracing · Model Deployment Options
Link last checked: 2026-09-24
Retrieval augmented generation (RAG) and indexes
Learn how retrieval augmented generation (RAG) uses indexes and grounding data to improve response accuracy in generative AI apps.
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: Retrieval-Augmented Generation (RAG) · Embedding · Vector Databases
Link last checked: 2026-09-24
Guardrails and controls overview in Microsoft Foundry
Learn about safety and security guardrails that can be applied to models and agents in Microsoft Foundry, including risks, intervention points, and response actions.
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 · AI Agent
Link last checked: 2026-09-24
How to configure guardrails and controls in Microsoft Foundry
Create, configure, and manage guardrails and controls for model deployments and agents in Microsoft Foundry using the portal or REST API.
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 · AI Agent
Link last checked: 2026-09-24
Intervention points
Learn about Agentic AI intervention points.
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 · AI Agent
Link last checked: 2026-09-24
Agentic Workflows: Task Adherence (preview)
Learn how to use Task Adherence to ensure AI agents align with user instructions and task objectives.
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 · AI Agent
Link last checked: 2026-09-24
Agent tracing overview
Learn how agent tracing in Microsoft Foundry captures inputs, outputs, and tool usage with OpenTelemetry. Debug agent runs, identify latency issues, and improve reliability.
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
Tracing and data handling
Tracing in Microsoft Foundry captures execution data to improve AI agent performance. Find out how to enable, disable, and govern trace data.
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
Use Insights in Foundry (preview)
Learn how to run Insights scans, review recurring production behaviors and supporting traces, and route confirmed Insights to evaluation or optimization 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: LLM Observability and Tracing · Model Deployment Options
Link last checked: 2026-09-24
Run benchmark evaluations in Microsoft Foundry (preview)
Learn how to use benchmark evaluations in Microsoft Foundry to evaluate model and agent quality against built-in benchmark datasets and metrics.
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