AI-3016: Develop generative AI apps by using Microsoft Foundry (APL-3016 prep)
Rebuild the Halcyon Insurance claims copilot end-to-end on Microsoft Foundry and prep for the APL-3016 Applied Skills assessment: account + project + connections, model deployment, Microsoft Agent Framework, RAG, responsible AI, evaluation, and a capstone.
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Exam Preparation Included
Practice with real exam-style questions for the AI-3016 certification. AI-powered feedback helps you understand every answer.
About This Course
Ship a production-quality generative AI copilot on Microsoft Foundry and prep for the APL-3016 Applied Skills assessment. Over 11 lessons you'll rebuild the fictional Halcyon Insurance claims-adjuster copilot end-to-end Foundry account + project + connected resources, model deployment, Microsoft Agent Framework orchestration, RAG grounding against a policy-document library, custom content-safety filters, quality + risk-safety evaluation, and a full end-to-end capstone. Every artifact is grounded against the current Microsoft Foundry product (AIServices topology, Agent Framework replacing Prompt Flow, current model versions).
Course Curriculum
11 Lessons
Microsoft Foundry: what it is and what you'll build
Iris introduces the Foundry topology: a Microsoft.CognitiveServices/accounts resource with kind: AIServices and allowProjectManagement: true, Foundry projects as sub-resources, and typed connections to Storage and AI Search. She reconciles the APL-3016 exam-blueprint vocabulary (AI Studio, AI hub, AI project, prompt flow) with the current Foundry SDK and portal names so students can read the study guide and build with the current tooling.
Students leave able to describe the exact Foundry stack Lab 2509 provisions for Halcyon Assist.
Set up your Foundry account, project, connections, and models
Students provision the Halcyon Assist Foundry stack by hand. Using the Azure CLI, they run az cognitiveservices account create with kind AIServices, then az cognitiveservices account project create for halcyon-project. They deploy gpt-5.1 and text-embedding-3-large, and add AI Search plus Storage connections.
After this lab, students can stand up a Foundry project from scratch that later labs build on.
Copilot patterns with Microsoft Agent Framework
Iris teaches the five-layer copilot mental model — Grounding, Model, Orchestration, Tools, Session — and maps each layer to Microsoft Agent Framework primitives: FoundryChatClient, Agent, AgentThread, function tools, and streaming via agent.run_stream(...). She walks through how Halcyon Assist will be built as an Agent Framework agent and shows when to reach for a tool-using agent versus a WorkflowBuilder pipeline.
Students leave ready to wire the code in Lab 2511 and know why each layer exists.
Build the Halcyon Assist copilot
This lab pre-deploys resources and may take 15 or more minutes to start.
Build the first working version of the Halcyon Assist claims-adjuster copilot against a pre-deployed Foundry stack (account, halcyon-project, gpt-5.1, gpt-5-mini, text-embedding-3-large). Students author the system prompt, wire AIProjectClient, rewrite in Microsoft Agent Framework, add a lookup_policy_summary function tool, and enable streaming with AgentThread.
Retrieval-Augmented Generation: grounding LLMs against your data
Iris explains why LLMs hallucinate and how Retrieval-Augmented Generation fixes it. She covers vector, keyword, and hybrid retrieval; Azure AI Search integrated vectorization; the semantic ranker; embedding models like text-embedding-3-large; chunking strategies; and citation patterns that let a copilot cite its sources.
Students leave ready to ground Halcyon Assist against the policy library in Lab 2513.
Add RAG to Halcyon Assist
This lab pre-deploys resources and may take 15 or more minutes to start.
Ground Halcyon Assist against Halcyon Insurance's policy library. Students upload homeowners, auto, and umbrella PDFs to the pre-created halcyon-policies blob container, build an integrated-vectorization index through the Foundry portal Import wizard using text-embedding-3-large, wire get_azure_ai_search_tool onto the Agent Framework agent, and compare grounded versus ungrounded answers.
Responsible AI in Foundry: filters, blocklists, and safety policies
Iris teaches Foundry's Responsible AI controls: input and output content filters for Hate, Sexual, Violence, and Self-harm; blocklists with exact and regex matching; protected-material detection for text and code; and the raiPolicies ARM resource type. She contrasts the default policy shipped with every deployment against custom policies with tighter thresholds.
Students leave ready to author and attach a custom filter in Lab 2515.
Custom content filters for Halcyon Assist
This lab pre-deploys resources and may take 15 or more minutes to start.
Students author a custom Responsible AI policy for Halcyon Assist in the Foundry portal: an input blocklist for competitor names and a PII regex, output filters with protected-material detection for text and code, and tightened Hate/Sexual/Violence thresholds. They attach the raiPolicy to the gpt-5.1 deployment and verify the guardrails from Python end-to-end.
Evaluate copilot performance
Iris teaches how to prove a copilot is production-ready. She covers building a golden dataset, running Foundry's quality evaluators (Relevance, Groundedness, Coherence, Fluency), layering risk and safety evaluators, reproducing runs with the azure-ai-evaluation SDK, and combining aggregate scores with manual side-by-side review.
Students leave ready to assess Halcyon Assist and pick weak categories to iterate on in Lab 2517.
Assess Halcyon Assist
This lab pre-deploys resources and may take 15 or more minutes to start.
Students run a full evaluation cycle on Halcyon Assist. From a pre-seeded halcyon-eval-golden blob container, they capture a golden dataset, run Foundry portal evaluations against Relevance/Groundedness/Coherence/Fluency plus risk and safety evaluators, reproduce the run with azure-ai-evaluation, and do a v1-vs-v2 side-by-side to identify weak categories.
Applied Skills capstone — end-to-end Halcyon Assist
This lab pre-deploys resources and may take 15 or more minutes to start.
End-to-end APL-3016 capstone across all seven Applied Skills tasks. Students extend Halcyon Assist with an estimate_renewal_quote function tool, extend the RAG index with renewal-terms and fee-schedule docs, author a tightened Responsible AI policy, run a full evaluation sweep, and complete the APL-3016 assessment report. Iris coaches from the sidebar but does not hand out solutions.
This course includes:
- 24/7 AI Instructor Support
- Live Lab Environments
- 6 Hands-on Lessons
- Exam Prep Included
- Completion Badge
Earn Your Badge
Complete all lessons to unlock the AI-3016: Develop generative AI apps with Microsoft Foundry (APL-3016) achievement badge.
AI-3016: Develop generative AI apps with Microsoft Foundry (APL-3016)
Awarded for completing the AI-3016 course — building a production-quality generative AI copilot on Microsoft Foundry end-to-end (Foundry account + project, model deployment, Microsoft Agent Framework, RAG, custom content-safety filters, evaluation, and an end-to-end capstone) and prepping for the APL-3016 Applied Skills assessment.
Skills You'll Earn
Complete all lessons in this course to earn this badge