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AI and Machine Learning premium Microsoft Foundry Microsoft Foundry is the Microsoft AI platform experience used to build, evaluate, deploy, and manage AI applications, models, agents, and related project assets. Teams should manage it with clear ownership, monitoring, rollback evidence, and production change discipline. AI platform intermediate 4 commands Aliases: Azure AI Foundry, Foundry, AI Foundry Quick peek Open full term page

AI and Machine Learning premium Foundry Agent A Foundry Agent is an AI agent built or hosted in Microsoft Foundry Agent Service with instructions, model selection, tools, knowledge, and runtime execution evidence. Teams use it to automate business tasks that require model reasoning, tool calls, enterprise knowledge, workflow steps, retrieval, approvals, and observable runs inside a governed Azure AI platform. It is not a magic autonomous worker, a security principal by itself, a replacement for process controls, or a safe production feature without tool permissions, evaluation, monitoring, and human escalation. Microsoft Foundry intermediate 6 commands Aliases: Microsoft Foundry agent, Foundry Agent Service agent, AI agent in Foundry, agent service agent Quick peek Open full term page

AI and Machine Learning premium Foundry hub A Foundry hub is a hub-style Microsoft Foundry resource used in selected scenarios to group AI projects with shared security, connections, data access, and governance settings. Teams use it to organize related AI projects that need common connections, managed identities, network controls, storage, security review, and platform governance across teams or application portfolios. It is not a whole Azure tenant, a model deployment by itself, a replacement for project-level permissions, or the required resource shape for every modern Foundry scenario. Microsoft Foundry intermediate 6 commands Aliases: Azure AI Foundry hub, Foundry hub resource, hub-based project, AI hub Quick peek Open full term page

AI and Machine Learning premium Foundry IQ Foundry IQ is a managed knowledge layer for Microsoft Foundry that connects enterprise data into reusable, permission-aware knowledge bases for AI agents. Teams use it to ground agents in approved enterprise knowledge from Azure, SharePoint, OneLake, the web, and Azure AI Search-backed knowledge bases while preserving permissions and reusable retrieval configuration. It is not a general data lake, a replacement for source-system permissions, a guarantee that retrieved content is correct, or a reason to skip citation, freshness, and access reviews. Microsoft Foundry intermediate 6 commands Aliases: Microsoft Foundry IQ, Foundry IQ knowledge base, managed knowledge layer, permission-aware knowledge base Quick peek Open full term page

AI and Machine Learning premium Azure AI Foundry Create a governed project boundary where AI teams can build agents, evaluations, files, and model deployments without unmanaged sprawl.; Compare, evaluate, and approve model deployments before a generative AI feature is AI platform fundamentals 5 commands Aliases: AI Foundry, Microsoft Foundry, Foundry resource, Foundry project, Azure AI Foundry, azure ai foundry Quick peek Open full term page

AI and Machine Learning premium Foundry Local Foundry Local is an on-device AI runtime and SDK for shipping applications that run curated generative AI models locally on user hardware. Microsoft Foundry intermediate 5 commands Aliases: Microsoft Foundry Local, Foundry Local CLI, on-device AI inference, local AI runtime Quick peek Open full term page

AI and Machine Learning premium Foundry Models Foundry Models is the Microsoft Foundry model catalog and deployment experience for discovering, evaluating, and using cloud AI models through managed endpoints. Microsoft Foundry intermediate 5 commands Aliases: Microsoft Foundry Models, Foundry model catalog, AI Foundry models, model catalog Quick peek Open full term page

AI and Machine Learning premium Foundry project A Foundry project organizes agents, model deployments, evaluations, files, and team access inside a Microsoft Foundry resource for building AI applications. Microsoft Foundry intermediate 5 commands Aliases: Microsoft Foundry project, AI Foundry project, Foundry workspace project Quick peek Open full term page

AI and Machine Learning premium Foundry resource A Foundry resource is the primary Azure resource for building, deploying, and managing generative AI models, agents, evaluations, and applications. Microsoft Foundry intermediate 5 commands Aliases: Microsoft Foundry resource, Foundry AIServices resource, AI Services Foundry resource Quick peek Open full term page

AI and Machine Learning premium Azure AI Foundry hub Azure AI Foundry hub is the classic Foundry hub resource used to group AI projects that share common security, data access, connections, and platform settings. In Azure, teams encounter it when teams need hub-based projects for selected Foundry and Azure Machine Learning scenarios such as fine-tuning, shared connections, and custom ML work. The useful question AI platform advanced 4 commands Aliases: AI Hub, Foundry AI Hub, hub-based project, Azure AI hub Quick peek Open full term page

AI and Machine Learning premium Azure AI Foundry project An Azure AI Foundry project is the working area where a team builds and organizes an AI application inside Microsoft Foundry. It keeps related agents, evaluations, files, indexes, tools, connections, and model usage together instead of scattering them across a shared portal. Think of it as the project boundary for one AI product, prototype, or team. It is useful because AI work quickly becomes messy: prompts, test data, model deployments, safety checks, and access decisions all need a home with ownership and repeatable operations. AI platform advanced 4 commands Aliases: AI Foundry project, AI project, Foundry project, Microsoft Foundry project Quick peek Open full term page

AI and Machine Learning premium Azure OpenAI resource An Azure OpenAI resource is the Azure control-plane boundary that hosts deployments, endpoints, access, networking, and billing context. Azure OpenAI fundamentals 12 commands Aliases: No aliases yet Quick peek Open full term page

AI and Machine Learning premium Azure OpenAI Service Azure OpenAI Service provides managed access to OpenAI model capabilities through Azure endpoints and enterprise controls. Generative AI fundamentals 11 commands Aliases: Azure OpenAI Quick peek Open full term page

AI and Machine Learning premium Azure OpenAI Azure OpenAI provides OpenAI model capabilities through Azure resources, deployments, identity, networking, quota, and monitoring controls. Azure OpenAI intermediate 8 commands Aliases: Azure OpenAI Service Quick peek Open full term page

AI and Machine Learning verified Prompt flow Prompt flow is a way to build and test an AI application as a flow instead of a single prompt. A flow can connect prompts, Python logic, tools, inputs, outputs, variants, and evaluation steps. In Microsoft Foundry classic and Azure Machine Learning, it helped teams prototype, debug, compare, deploy, and monitor LLM-based applications. In 2026, it is also a migration topic because Microsoft announced that feature development ended and full retirement is planned for April 20, 2027. Azure Machine Learning advanced 18 commands Aliases: No aliases yet Quick peek Open full term page

AI and Machine Learning premium Azure OpenAI managed identity Azure OpenAI managed identity uses Microsoft Entra identities so Azure workloads can call model endpoints without stored keys. Azure OpenAI intermediate 5 commands Aliases: No aliases yet Quick peek Open full term page

AI and Machine Learning premium Chat completion A AI platform capability in Azure OpenAI that helps teams build, secure, observe, and govern intelligent applications with clearer ownership, safety, and operational context. Azure OpenAI fundamentals 5 commands Aliases: No aliases yet Quick peek Open full term page

AI and Machine Learning premium Agent run Agent run is one execution of an agent against a thread, during which the agent reads messages, reasons, may call tools, and produces output. In everyday Azure work, teams use it to track what an agent attempted, what tools it used, whether it completed, and why a response was produced. The useful evidence is thread Microsoft Foundry intermediate 4 commands Aliases: Foundry agent run, AI agent run, thread run Quick peek Open full term page

AI and Machine Learning premium Agent service Agent service is Microsoft Foundry Agent Service, a managed platform for building, deploying, and scaling AI agents. In everyday Azure work, teams use it to create prompt agents, workflow agents, or hosted code-based agents that use models and tools to perform tasks. The useful evidence is project, agent ID, instructions, model, tool configuration, deployment type, Microsoft Foundry intermediate 4 commands Aliases: Microsoft Foundry Agent Service, Foundry Agent Service, Azure AI agent service Quick peek Open full term page

AI and Machine Learning premium Agent thread Agent thread is the persistent conversation state that stores messages and context used by an agent during runs. In everyday Azure work, teams use it to keep a multi-turn interaction organized so the agent can understand prior user messages and produce contextual responses. The useful evidence is thread ID, messages, metadata, related run IDs, timestamps, Microsoft Foundry intermediate 4 commands Aliases: Foundry agent thread, AI agent conversation thread, agent conversation context Quick peek Open full term page

AI and Machine Learning premium Agent tool Agent tool is a capability an agent can invoke to search, run code, query data, call APIs, or perform a configured action. In everyday Azure work, teams use it to let an agent go beyond text generation by grounding answers or carrying out controlled work. The useful evidence is tool type, configuration, authentication method, allowed Microsoft Foundry intermediate 4 commands Aliases: Foundry agent tool, AI agent tool, agent built-in tool, agent custom tool Quick peek Open full term page

AI and Machine Learning premium AI connection AI connection is a Microsoft Foundry project connection that links the project to an external or Azure resource such as models, storage, search, or services. In everyday Azure work, teams use it to let AI applications and agents use approved resources without every prototype hardcoding endpoints and credentials. The useful evidence is connection name, target AI platform intermediate 4 commands Aliases: Foundry connection, AI project connection, connected resource, AI service connection Quick peek Open full term page

AI and Machine Learning premium Azure AI metrics Azure AI metrics is the measurable signals used to observe Azure AI applications, model endpoints, agents, evaluations, safety checks, and business outcomes. Azure AI services intermediate 4 commands Aliases: AI metrics, AI service metrics, Azure AI Metrics Advisor, Azure Monitor metrics for AI, Metrics Advisor, time series anomaly detection Quick peek Open full term page

AI and Machine Learning premium Deployment type Deployment type is the Azure AI or Microsoft Foundry model hosting option, such as standard, global, data-zone, regional, batch, serverless, or provisioned deployment, that determines availability, data processing location, capacity model, cost, and operational behavior. Microsoft Foundry intermediate 4 commands Aliases: Microsoft Foundry deployment type, Azure AI deployment type, model deployment type, Foundry Models deployment type Quick peek Open full term page

AI and Machine Learning premium Fine-tuning Fine-tuning is the process of customizing a supported model with curated training examples so it performs better for a specific task in Azure. Azure AI Foundry and Azure OpenAI intermediate 4 commands Aliases: Fine-tuning, fine tuning Quick peek Open full term page

AI and Machine Learning premium Model deployment Model deployment is the step where a trained or selected model becomes something an application can actually call. The model may have performed well in experiments, but deployment decides how it is hosted, secured, scaled, monitored, and routed. Generative AI fundamentals 4 commands Aliases: No aliases yet Quick peek Open full term page

AI and Machine Learning premium Multi-service AI account Multi-service AI account means a shared Azure AI services account for applications that need several supported AI capabilities from one governed endpoint boundary. You see it when teams connect language, vision, speech, translation, or content-understanding features to production applications. Think of it as one managed account boundary for several AI capabilities, not one unlimited permission bucket. It matters because the setting changes how teams design, secure, operate, and troubleshoot the workload. Before changing it in production, know the owner, dependency, evidence, expected result, and rollback path. Azure AI services intermediate 4 commands Aliases: No aliases yet Quick peek Open full term page

AI and Machine Learning premium Multi-service AI resource Multi-service AI resource means an Azure AI resource that exposes supported AI service capabilities through one managed resource object. You see it when teams build applications that combine language, vision, speech, translation, or content services without creating a separate resource for every feature. Think of it as one Azure resource boundary for supported AI capabilities, with identity, network, monitoring, and billing attached. It matters because the setting changes how teams design, secure, operate, and troubleshoot the workload. Before changing it in production, know the owner, dependency, evidence, expected result, and rollback path. Azure AI services fundamentals 4 commands Aliases: No aliases yet Quick peek Open full term page

AI and Machine Learning premium AI evaluation AI evaluation is the Microsoft Foundry process of testing a generative AI model, agent, or application against a dataset and measuring its quality, safety, and task performance with built-in or custom evaluators. Microsoft Foundry intermediate 3 commands Aliases: Azure AI evaluation, Foundry evaluation, generative AI evaluation, model evaluation Quick peek Open full term page

AI and Machine Learning premium AI quota AI quota is the Azure allocation that limits model deployment capacity, usually by subscription, region, model, and deployment type, using measures such as tokens per minute, requests per minute, concurrent requests, or provisioned throughput. Microsoft Foundry intermediate 3 commands Aliases: Foundry quota, Azure OpenAI quota, model quota, TPM quota, RPM quota Quick peek Open full term page

AI and Machine Learning premium AI red teaming AI red teaming is the practice of probing a generative AI system with adversarial prompts, attack strategies, and risk categories to discover unsafe behavior, jailbreak weaknesses, and harmful-output paths before or after deployment. Responsible AI advanced 3 commands Aliases: AI Red Teaming Agent, red team scan, adversarial AI testing, LLM red teaming Quick peek Open full term page

AI and Machine Learning premium AI services account key An AI services account key is a secret key for a multi-service Azure AI or Microsoft Foundry resource. It can authenticate requests for supported services tied to that resource, making storage, rotation, and access control especially important. Azure AI services intermediate 3 commands Aliases: multi-service account key, Foundry resource key, AIServices account key, Cognitive Services account key Quick peek Open full term page

AI and Machine Learning premium AI services endpoint An AI services endpoint is the base URL for a multi-service Azure AI or Microsoft Foundry resource. Applications combine this endpoint with a supported API path and authentication method to call the resource. Azure AI services fundamentals 3 commands Aliases: multi-service endpoint, Foundry resource endpoint, AIServices endpoint, Cognitive Services endpoint Quick peek Open full term page

AI and Machine Learning premium AI tracing AI tracing captures execution telemetry for generative AI applications and agents, including model calls, prompts, tool invocations, retrieval steps, latency, token usage, errors, and run relationships so teams can debug and monitor behavior in Microsoft Foundry and Azure Monitor. AI observability intermediate 3 commands Aliases: Foundry tracing, agent tracing, LLM tracing, AI distributed tracing Quick peek Open full term page

AI and Machine Learning premium Chat completions Chat completions are API calls to chat-capable models where the client sends role-based messages and receives the next model response, commonly through Azure OpenAI or Microsoft Foundry endpoints. Azure OpenAI intermediate 3 commands Aliases: No aliases yet Quick peek Open full term page

AI and Machine Learning premium Code interpreter tool An agent tool that can execute code for analysis tasks. AI platform and search intermediate 3 commands Aliases: No aliases yet Quick peek Open full term page

AI and Machine Learning premium Completion the generated text or structured output returned by an AI model after an application sends prompt, message, or inference request data Azure OpenAI Intermediate 3 commands Aliases: No aliases yet Quick peek Open full term page

AI and Machine Learning premium Content Understanding Content Understanding is documented by Microsoft as part of the Azure AI services area in Azure. Azure AI services intermediate 3 commands Aliases: No aliases yet Quick peek Open full term page

AI and Machine Learning verified Prompt A prompt is what you give an AI model so it knows what to do. Generative AI fundamentals 6 commands Aliases: No aliases yet Quick peek Open full term page

AI and Machine Learning verified Prompt evaluation Prompt evaluation is how teams test whether a prompt actually works instead of relying on a good demo. You run the AI behavior against a dataset of representative inputs, expected answers, safety cases, or business rules. Evaluators score quality, grounding, safety, formatting, and task success. In Azure and Microsoft Foundry, prompt evaluation helps decide whether a prompt, model, agent, or retrieval change is ready for production. It turns subjective “looks good” reviews into measurable evidence. Azure Machine Learning intermediate 6 commands Aliases: No aliases yet Quick peek Open full term page

AI and Machine Learning verified Prompt Shields Prompt Shields is a Microsoft Foundry and Azure AI Content Safety capability that detects adversarial inputs against language-model applications. It covers direct user prompt attacks and indirect document attacks, helping apps identify attempts to override instructions, exfiltrate data, or manipulate model behavior before generation. Azure AI services intermediate 6 commands Aliases: No aliases yet Quick peek Open full term page

AI and Machine Learning verified Provisioned throughput for AI Provisioned throughput for AI in Microsoft Foundry allocates provisioned throughput units to supported model deployments. PTUs give reserved processing capacity for predictable latency and throughput, with deployment choices such as regional, data-zone, or global provisioned capacity and separate reservation-based billing options. Microsoft Foundry advanced 6 commands Aliases: No aliases yet Quick peek Open full term page

AI and Machine Learning field-manual-complete Language service Azure Language is a cloud-based Azure AI service for natural language processing. It provides core text analysis capabilities such as language detection, PII detection, named entity recognition, text analytics for health, and other features through Microsoft Foundry, REST APIs, client libraries, containers, and agent tools. Azure AI services fundamentals 5 commands Aliases: No aliases yet Quick peek Open full term page

AI and Machine Learning verified Prompt engineering Prompt engineering is the craft of asking an AI model for work in a way it can follow reliably. It includes clear instructions, examples, context, boundaries, and required output formats. In Azure OpenAI and Foundry applications, prompt engineering is not magic phrasing; it is application design. A strong prompt explains the task, what evidence to use, what to avoid, and how success will be measured. It still needs testing because one good example does not prove the prompt works everywhere. AI platform and search intermediate 5 commands Aliases: No aliases yet Quick peek Open full term page

AI and Machine Learning verified Question answering Question answering lets users ask normal-language questions and receive answers from a curated knowledge base, such as FAQs, support articles, policies, or product documentation. Azure AI services intermediate 5 commands Aliases: No aliases yet Quick peek Open full term page

AI and Machine Learning verified RAG Retrieval-augmented generation grounds generative AI responses in retrieved enterprise content instead of relying only on model training. In Azure, RAG commonly combines Azure AI Search indexes, grounding data, embeddings, and an Azure OpenAI or Foundry model to answer using current, domain-specific information. Generative AI advanced 5 commands Aliases: Retrieval augmented generation, retrieval-augmented generation Quick peek Open full term page

AI and Machine Learning verified RAG evaluation RAG evaluation measures whether a retrieval-augmented system finds relevant grounding documents and produces answers that are grounded, relevant, coherent, and safe. Azure AI Foundry includes RAG evaluators for assessing retrieval quality and generation quality before releasing or changing an AI experience. Microsoft Foundry intermediate 5 commands Aliases: retrieval augmented generation evaluation, RAG evaluators Quick peek Open full term page

AI and Machine Learning template-specs-upgraded Retrieval augmented generation Microsoft Learn describes retrieval augmented generation as an AI pattern where a user query retrieves relevant grounding content, often from Azure AI Search or Foundry indexes, before a model generates an answer. The goal is more accurate, cited, and current responses from private or changing data. Grounded generation fundamentals 5 commands Aliases: RAG, retrieval augmented generation, grounded generation, Azure AI Search RAG, agentic RAG Quick peek Open full term page

AI and Machine Learning template-specs-upgraded Retrieval tool Microsoft Learn describes the file search tool for Microsoft Foundry agents as a retrieval tool that lets an agent search uploaded or connected documents, create vector stores, and ground responses in external knowledge. Standard setup uses connected Azure AI Search and Blob Storage resources. Agents and grounding intermediate 5 commands Aliases: file search tool, agent retrieval tool, Foundry retrieval tool, vector store tool, knowledge retrieval tool Quick peek Open full term page

AI and Machine Learning template-specs-upgraded Safety system message Microsoft Learn explains that safety system messages guide Azure OpenAI model behavior, improve response quality, and reduce the likelihood of harmful outputs. They are one layer in a broader safety strategy and are also described as system prompts or metaprompts. Microsoft Foundry intermediate 5 commands Aliases: Azure OpenAI safety system message, safety metaprompt, system prompt safety guidance, Foundry safety message, AI safety instructions Quick peek Open full term page