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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
Testimonials (1)
the instructor :)