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Course Outline

Foundations of Azure OpenAI for Institutional Use

  • Overview of Azure OpenAI architecture and its integration within the broader Azure cloud ecosystem
  • Identification of high-impact generative AI use cases applicable to public and private sector operations
  • Analysis of core model capabilities, functional specifications, and architectural solution patterns

Initial Configuration of Azure OpenAI Services

  • Provisioning of Azure OpenAI resources, establishing access controls, and selecting appropriate deployment strategies
  • Evaluation of the interactive playground and available API interfaces for development testing
  • Execution of test prompts and systematic assessment of generated outputs for accuracy and relevance

Prompt Engineering for Organizational Objectives

  • Construction of precise directives and structured input frameworks to optimize AI performance
  • Application of role definition, few-shot examples, and standardized output formatting protocols
  • Strategies for enhancing output consistency in tasks such as document summarization, knowledge retrieval, and content generation

Development and Integration of AI Capabilities

  • Implementation of conversational interfaces, summarization tools, and content assistance modules within existing software
  • Technical methods for invoking Azure OpenAI services from backend systems and integrated service layers
  • Design of user interaction workflows that align with operational goals and deliver measurable business value

Integration of AI with Institutional Data Repositories

  • Conceptual understanding and practical application of Retrieval-Augmented Generation (RAG) architectures
  • Utilization of Azure AI Search in conjunction with Azure OpenAI to enhance information retrieval
  • Ensuring response accuracy and integrity by grounding AI outputs in verified institutional knowledge bases

Security Governance, Responsible AI, and Operational Management

  • Implementation of access management protocols, data privacy safeguards, and secure deployment practices
  • Adherence to content safety standards, establishment of human oversight mechanisms, and application of responsible AI frameworks
  • Continuous monitoring of resource utilization, output quality, and cost efficiency in production environments

Practical Implementation Workshop and Program Summary

  • Development of a prototypical enterprise AI solution to demonstrate core competencies
  • Validation of prompt effectiveness, data grounding accuracy, and final output quality through rigorous testing
  • Formulation of strategic next steps for pilot implementation and transition to full-scale production use

Requirements

  • Proficiency in fundamental cloud computing and web application development concepts
  • Demonstrated familiarity with Azure service offerings, RESTful APIs, or application integration patterns
  • Foundational programming experience in a supported language

Target Audience

  • Application Developers
  • Solution Architects
  • Technical Managers and Innovation Leaders
 7 Hours

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