Azure AI Foundry Intensive for DIA Developers (AI-3016)
Two-day Azure AI Foundry intensive for DIA developers. Eight teaching+hands-on pairs covering Foundry projects, model catalog, Responses API, tools, RAG with Azure AI Search, production observability, and an end-to-end capstone. Fictional SIB OSINT scenario, non-classified.
About This Course
A two-day intensive for Defense Intelligence Agency developers, covering Microsoft Azure AI Foundry end-to-end: the Foundry portal and projects, the model catalog and evaluation, chat applications with the OpenAI Responses API, function calling and tools, prompt engineering and Retrieval-Augmented Generation (RAG), vector stores and grounded retrieval, production observability and responsible AI governance, and a capstone integrating chat, tools, RAG, and tracing into one Foundry project. All scenarios are framed around a fictional, non-classified Sentinel Intelligence Bureau (SIB) OSINT Modernization team and use only synthesized open-source-style data.
Course Curriculum
16 Lessons
Microsoft Foundry and Project Fundamentals (Teaching)
Agent-led introduction to Microsoft Foundry, AI Services accounts, projects, the Foundry portal, and how SIB's OSINT Modernization team organizes resources. Includes assessments. No exercises.
Create a Foundry Project and Deploy gpt-5 — Lab Exercises
Hands-on lab. Create a Foundry project, deploy gpt-5, and test it in the model playground from the perspective of an SIB OSINT engineer setting up a new working project.
Model Catalog, Benchmarks, and Evaluation (Teaching)
Agent-led teaching on the Foundry model catalog, benchmark cards, side-by-side comparison, and synthetic evaluation against a small dataset — for an SIB analyst-facing OSINT scenario.
Compare Models and Run a Synthetic Evaluation — Lab Exercises
Hands-on lab. Compare gpt-5 and gpt-5-mini in the catalog and the side-by-side playground, deploy both, and run a small Foundry Evaluations job over a synthetic SIB OSINT dataset.
Chat Apps with the Responses API (Teaching)
Agent-led teaching on the OpenAI Responses API via AIProjectClient: request lifecycle, output structure, state via previous_response_id, streaming, and Entra ID keyless authentication.
Implement a Chat App with the Responses API — Lab Exercises
Hands-on lab. Implement POST /chat and POST /chat/stream in the SIB OSINT Concierge FastAPI service using AIProjectClient.get_openai_client() and the Responses API. Uses the intel-chat-tools starter.
Function Calling and Tools (Teaching)
Agent-led teaching on Responses-API tool schemas (flat shape), the tool-call loop, function_call_output items, web_search and file_search built-in tools, and choosing custom function tools for OSINT workflows.
Implement the Tool-Call Loop — Lab Exercises
Hands-on lab. Wire the tool-call loop in the SIB OSINT Concierge so the model can invoke get_open_source_news, calculate, and lookup_threat_feed. Then add file_search to ground answers in handbook excerpts. Uses the intel-chat-tools starter.
Prompt Engineering and RAG Fundamentals (Teaching)
Agent-led teaching on prompt engineering patterns for grounded answering, the RAG architecture, document chunking trade-offs, and why SIB analyst products require citable sources.
Build an Azure AI Search Index and Ingest Documents — Lab Exercises
Hands-on lab. Define the sib-osint-rag Azure AI Search index (vector + semantic ranker), ingest and embed the SIB handbook and policy docs, and run a first hybrid + semantic search. Uses the intel-rag-eval starter.
Vector Stores and Grounded Retrieval (Teaching)
Agent-led teaching on vector store options (Azure AI Search vs pgvector), embedding model selection, hybrid retrieval mechanics, semantic ranker behavior, and evaluation metrics (groundedness, relevance).
Build a RAG Chat Endpoint with Groundedness Evaluation — Lab Exercises
Hands-on lab. Implement POST /chat as a grounded retrieval flow over the SIB index, then score the system with GroundednessEvaluator and RelevanceEvaluator against an evaluation dataset. Uses the intel-rag-eval starter.
Production, Monitoring, and Responsible AI Governance (Teaching)
Agent-led teaching on Foundry content filters, Application Insights tracing for AI apps, cost and latency monitoring, model-version pinning, and responsible AI / governance considerations for a government OSINT context.
Configure Content Filters and Application Insights — Lab Exercises
Hands-on lab. Configure a custom Foundry content filter for the SIB Concierge and wire Azure Monitor OpenTelemetry into a small FastAPI service so every Responses call shows up as a distributed trace in Application Insights.
Capstone Overview — End-to-End Foundry Pipeline (Teaching)
Agent-led capstone overview: stitches chat, tools, RAG, and tracing into one Foundry project for the SIB OSINT Concierge. Final review of patterns and an integration plan before the hands-on capstone.
Capstone: Chat, Tools, RAG, and Tracing on One Foundry Project — Lab Exercises
Hands-on capstone. Build the SIB OSINT Concierge end-to-end: /health, /chat, /rag, /agent (tool-using), and Application Insights tracing — all on one Foundry project. Uses the intel-capstone starter.