Enterprise GenAI App Devt. with Azure OpenAI | Azure AI Foundry

Design, build, and operationalize secure, scalable Generative AI applications on Azure—from prompts and RAG pipelines to production-grade deployment.

  • Level : Intermediate to Advanced
  • Duration : 32 hours
  • Hands-on Ratio : 65–75%
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Building Strategic Influence in Matrix Organizations

Building enterprise-grade GenAI applications requires more than model access—it demands architecture, governance, and integration discipline. Enterprise GenAI App Development with Azure OpenAI & Azure AI Foundry equips teams to design and deploy secure, scalable GenAI solutions on Azure.

The course covers application patterns using Azure OpenAI, Azure AI Foundry, and supporting Azure services. Participants focus on prompt design, orchestration, grounding, security controls, and enterprise integration considerations.

Strong emphasis is placed on operational readiness, including access control, monitoring, and lifecycle management. The outcome is practical capability to build GenAI applications that meet enterprise standards for reliability, security, and maintainability.

Business Outcomes

Organizations enrolling teams in this course can achieve

  • Production-Ready GenAI Applications: Faster delivery of secure, scalable Azure-based GenAI solutions that meet enterprise architecture and operational standards
  • Reduced Engineering and Operational Risk: Built-in security, safety, observability, and evaluation practices that lower the risk of outages, misuse, and cost overruns
  • Repeatable Delivery at Scale: Standardized engineering patterns for prompts, RAG, workflows, agents, and deployment that support consistent ROI across teams

Why You Shouldn’t Miss this course

By the end of this course, participants will be able to:
  • Understand Azure GenAI architecture patterns using Azure OpenAI and Azure AI Foundry for enterprise applications
  • Apply PromptOps practices including structured prompting, prompt flows, testing, and evaluation
  • Analyze retrieval quality, performance, and grounding strategies in Retrieval-Augmented Generation systems
  • Create tool-using workflows, agentic applications, and production-grade APIs for GenAI solutions
  • Implement secure, observable, and cost-optimized GenAI applications using Azure-native services

Recommended Experience

Participants should be proficient in Python, comfortable working with REST APIs, and familiar with core Azure fundamentals such as resource groups, identity, and networking. Prior experience with Git-based workflows is expected, along with exposure to basic cloud-native application development concepts.

Structured for Strategic Application

Bloom-aligned objectives 
  • Understand: core Azure GenAI building blocks and how they compose into enterprise apps 
  • Analyze: reference architectures for chatbots, copilots, and workflow assistants 
  • Design: a target architecture with clear boundaries (model, retrieval, tools, app tier, ops) 
Topics 
  • Azure AI Foundry concepts: projects, model deployments, app development lifecycle (dev → eval → deploy)  
  • Azure OpenAI role in the stack (chat completions, embeddings) and common enterprise app patterns 
  • Architecture patterns 
  • Chatbots and knowledge assistants (RAG-based) 
  • Copilot-style experiences (task-focused, tool-using) 
  • Workflow assistants (event-driven, approvals, deterministic steps) 
  • Non-functional requirements baseline: latency, availability, auditability, data boundaries 
Labs 
  • Lab 1.1: Solution blueprint — Draft a reference architecture and component diagram for a chosen pattern (RAG chatbot / workflow assistant / copilot-like agent). 
  • Lab 1.2: Azure resource plan — Define the minimal secure Azure resource set (Foundry/OpenAI, Search, App runtime, Key Vault, Monitor). 
Bloom-aligned objectives 
  • Apply: advanced prompting techniques for controllability and reliability 
  • Create: prompt flows that orchestrate prompts, tools, and code 
  • Evaluate: prompts and flows using test datasets and evaluation runs 
Topics 
  • Prompt engineering for production 
  • role + task + constraints + format contracts 
  • structured outputs (JSON schemas), deterministic templates, refusal behaviors 
  • mitigation patterns: ambiguity handling, self-check prompts, “unknown” routing 
  • prompt flow in Azure AI Foundry 
  • building executable flows that chain prompts, Python tools, and integrations  
  • variants, debugging, iteration cycles 
  • PromptOps 
  • prompt versioning strategy, regression tests, golden datasets 
  • Prompt versioning, registry, evaluation and optimization using mlflow 
  • evaluation runs and interpreting evaluation metrics in Foundry 
Labs 
  • Lab 2.1: Prompt contract pack — Build 6 reusable prompt templates (summarize, extract, classify, rewrite, generate plan, validate). 
  • Lab 2.2: Prompt flow pipeline — Implement a multi-step prompt flow: intake → extract entities → generate response → validator step.  
  • Lab 2.3: Prompt evaluation suite — Create a test dataset and run evaluations; compare variants and document a promotion decision.  
Bloom-aligned objectives 
  • Design: a RAG architecture with clear retrieval and citation strategy 
  • Implement: ingestion + chunking + embeddings + indexing in Azure AI Search 
  • Analyze: retrieval quality and tune chunking, query strategy, and grounding 
Topics 
  • RAG patterns and tradeoffs; why grounding is required for enterprise reliability  
  • Azure AI Search for RAG 
  • vector and hybrid retrieval concepts 
  • index design (metadata, filters, scoring) 
  • Content preparation pipeline 
  • document parsing and extraction 
  • chunking strategies, overlap, semantic boundaries 
  • Classic RAG vs agentic retrieval (when retrieval becomes iterative/tool-driven)  
  • Implementation blueprint using Microsoft’s RAG tutorial pattern (FastAPI + OpenAI + Search)  
Labs 
  • Lab 3.1: RAG ingestion pipeline — Build an ingestion job: parse documents → chunk → embed → index into Azure AI Search. 
  • Lab 3.2: Grounded chat API — Implement a FastAPI/Node API that performs retrieval + grounded answer generation with citations. 
  • Lab 3.3: Retrieval tuning — Improve answer quality by adjusting chunking/index schema and adding metadata filters; measure improvements with an evaluation set. 
Bloom-aligned objectives 
  • Apply: function calling and tool-use patterns for deterministic business workflows 
  • Create: orchestrated workflows that combine LLM steps with rules and validations 
  • Evaluate: reliability with negative tests and safe-fail behaviors 
Topics 
  • “Static workflow” design: deterministic orchestration around an LLM 
  • intake validation 
  • tool invocation 
  • policy checks 
  • final response assembly 
  • Tool/function calling patterns 
  • API tools (CRM lookup, ticket creation, approvals) 
  • retrieval tools (search, KB lookup) 
  • calculation/transform tools (Python, rules engine) 
  • Guardrails in workflows 
  • data minimization 
  • approval gates (draft vs execute) 
  • safe output constraints and redaction 
Labs 
  • Lab 4.1: Tool-using assistant — Build an assistant that calls 2 tools (retrieval + business API stub) and returns a structured result. 
  • Lab 4.2: Validation-first workflow — Implement a workflow that blocks unsafe/low-confidence outputs and routes to human review. 
Bloom-aligned objectives 
  • Understand: agent runtime concepts (threads, runs, messages, state) 
  • Implement: single-agent and multi-step agent behaviors with custom tools 
  • Design: agent boundaries, memory strategy, and escalation paths 
Topics 
  • Foundry Agent Service overview and why agents differ from single-shot chat 
  • Conversation state and persistence: threads/runs/messages 
  • Tool integration for agents 
  • custom tool invocation patterns 
  • safe tool execution and permission boundaries 
  • Orchestration patterns 
  • planner + executor 
  • critic/reviewer loop 
  • multi-agent handoffs (conceptual alignment to common agent design patterns) 
Labs 
  • Lab 5.1: Build an agent with persistent threads — Implement an agent that maintains state across turns and performs a multi-step task. 
  • Lab 5.2: Agent with custom tools — Add at least 2 tools (retrieval + action tool) and enforce execution constraints. 
  • Lab 5.3: Agentic RAG — Implement iterative retrieval (agent decides when to search again) and compare to classic single-pass RAG. 
Bloom-aligned objectives 
  • Implement: production-grade API design for GenAI apps (streaming, retries, timeouts) 
  • Deploy: an end-to-end application to Azure runtime services 
  • Analyze: performance bottlenecks and apply scaling patterns 
Topics 
  • API engineering for GenAI 
  • streaming responses, async execution, idempotency 
  • concurrency limits and backpressure 
  • caching strategies (prompt caching, retrieval caching) 
  • Deployment patterns (choose one for labs) 
  • Azure App Service / Azure Container Apps / AKS (containerized) 
  • Data stores for state and history (where needed) 
  • conversation state storage patterns (with clear retention) 
  • CI/CD and environment promotion 
  • dev/test/prod separation for prompts, indices, and policies 
Labs 
  • Lab 6.1: Deploy the RAG API — Deploy your RAG service to App Service or Container Apps with secure configuration. 
  • Lab 6.2: Performance drill — Add caching + request controls; document latency and throughput changes. 
Bloom-aligned objectives 
  • Apply: Azure OpenAI security baseline principles (identity, network, monitoring) 
  • Implement: network isolation (private endpoints) and least-privilege access 
  • Evaluate: prompt injection and jailbreak risks; apply mitigations 
Topics 
  • Azure OpenAI security baseline: control categories and operational expectations 
  • Network security: private endpoints and VNet integration patterns for Azure OpenAI  
  • Identity and access 
  • managed identities, RBAC, secrets management patterns 
  • Safety controls in Azure 
  • content filters and policy enforcement in Foundry workflows 
  • Prompt Shields (jailbreak/prompt injection defenses) in Azure AI Content Safety 
  • Threat modeling for GenAI apps 
  • data exfiltration via prompts 
  • indirect prompt injection via documents 
  • tool abuse and privilege escalation 
Labs 
  • Lab 7.1: Secure-by-default deployment — Add private endpoint planning + RBAC roles + secret handling checklist for your app. 
  • Lab 7.2: Prompt attack simulation — Run a set of jailbreak/injection test prompts; implement mitigations (Prompt Shields + validation gates). 
Bloom-aligned objectives 
  • Monitor: Azure OpenAI and app behavior using Azure Monitor/Log Analytics 
  • Evaluate: model/app quality continuously using Foundry evaluation runs 
  • Optimize: cost and performance with quotas, throttling strategy, and prompt efficiency 
Topics 
  • Monitoring Azure OpenAI with Azure Monitor 
  • diagnostics settings, Log Analytics, metrics, and workbooks 
  • Foundry evaluation operations 
  • running evaluations on models/agents/test datasets and reviewing metrics 
  • Cost and performance tuning 
  • token efficiency (prompt sizing, summarization, retrieval narrowing) 
  • batch vs realtime tradeoffs 
  • scaling and throttling strategy under load 
  • Operational playbook 
  • incident types (quality regressions, retrieval drift, safety events) 
  • release management for prompts/flows/indices 
Labs 
  • Lab 8.1: Monitoring dashboard — Configure diagnostic settings and create basic Log Analytics queries/workbook tiles for usage and errors. 
  • Lab 8.2: Quality regression gate — Set up an evaluation run as a release gate for prompt/flow changes and document go/no-go criteria.  
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Designed for Immediate Organizational Impact

Includes real-world simulations, stakeholder tools, and influence models tailored for complex organizations.

Instructor-Led Enterprise Training

Expert-led sessions focused on real-world Azure GenAI architecture, development, and operations.

Enterprise-Ready Use Cases

Hands-on scenarios covering chatbots, copilots, workflow agents, and retrieval-based applications.

High Hands-On Learning Ratio

Build labs, architecture workshops, and operational drills that result in deployable artifacts.

Responsible & Scalable AI Adoption

Integrated focus on security, safety, observability, and cost management for production environments.

This course follows Cognixia’s AI-first, hands-on learning model

Azure subscription with permissions to create resources; Azure OpenAI and Azure AI Foundry access; ability to deploy to App Service or Container Apps; Log Analytics workspace; sanitized document corpus for RAG labs

This course follows Cognixia’s AI-first, hands-on learning model—combining short concept sessions with practical labs, real workplace scenarios, and embedded governance to ensure safe, scalable, and effective skill adoption across the enterprise.

Let’s Connect

Speak with a Cognixia specialist about enrollment options, custom cohorts for your leadership team, or tailored delivery formats for your organization.

  • Response within 1 business day
  • Available in 5 delivery formats globally
  • Volume pricing for teams of 10+

Get in touch

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Frequently Asked Questions

Find details on duration, delivery formats, customization options, and post-program reinforcement.

Yes. This is a hands-on engineering course focused on building and operating GenAI applications on Azure.
Participants should have experience with Python, APIs, and Azure fundamentals. Prior GenAI experience is helpful but not mandatory.
Yes. The course is designed for consistent delivery across engineering, platform, and solution architecture teams.
Approximately 65–75% of the course is hands-on, including build labs, architecture design, and operational exercises.
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Why Cognixia for This Course

Cognixia delivers this course with a strong engineering and enterprise-operational focus, helping teams move beyond demos to production-ready GenAI systems on Azure. Our hands-on, artifact-driven delivery ensures participants build deployable components such as APIs, RAG pipelines, prompt evaluation suites, agents, and monitoring dashboards during the course. Cognixia embeds enterprise constraints—including security, identity, governance, observability, and cost controls—directly into the learning experience, ensuring solutions are realistic and production-safe. With extensive experience delivering large-scale, cloud and AI upskilling programs, Cognixia enables organizations to build durable GenAI engineering capabilities aligned with enterprise standards.

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