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Course Outline
Overview of GPT-5 Capabilities and Development Standards
- Core competencies of GPT-5, multimodal processing, and agentic functionality
- Model selection criteria, pricing structures, and operational limits
- Ethical frameworks and enterprise governance protocols for government
Prompt Engineering and System Architecture for Reliable Performance
- Prompt patterns, system directives, and context optimization
- Comparative analysis of chain-of-thought versus concise prompting and few-shot techniques
- Prompt validation processes and definition of acceptance criteria
API Integration, SDK Utilization, and Local Development Environments
- Invocation of GPT-5 APIs, SDK implementation, and secure credential management
- Local development practices, response simulation, and isolation techniques
- Version control, data schema standardization, and error handling mechanisms
Construction of Agentic Systems and External Tool Integration
- Architectural design for secure agent systems and standardized tool interfaces
- Request routing, workflow orchestration, and contingency strategies
- Rate limit management, concurrency controls, and transactional integrity
Testing, Evaluation, and Validation Protocols
- Automated testing frameworks for prompt consistency and behavioral assurance
- Red-teaming exercises, fuzz testing, and analysis of adversarial inputs
- KPIs for accuracy, hallucination mitigation, and user experience assessment
Deployment, Operational Monitoring, and Observability
- CI/CD methodologies for AI-enabled features and gradual release strategies
- Comprehensive logging, tracing, and telemetry for granular observability
- Alerting mechanisms, service level agreements (SLAs), and incident response procedures
Security, Privacy, and Cost Efficiency Optimizations
- Data management protocols, PII/PHI compliance, and context sanitization
- Access control measures, audit trails, and regulatory compliance checks
- Token consumption optimization, batch processing, and caching methodologies
Conclusion and Recommended Next Steps
Requirements
- Demonstrated proficiency in at least one programming language, such as Python or JavaScript
- Practical experience in invoking REST APIs or utilizing Software Development Kits (SDKs)
- Fundamental knowledge of Machine Learning (ML) and Artificial Intelligence (AI) concepts, as well as JSON data structures
Intended Audience
- Software Engineers
- Machine Learning (ML) Engineers
- DevOps and Site Reliability Engineering (SRE) Professionals
14 Hours
Testimonials (1)
Able to pivot upon audience suggestions - ie able to create a real AI agent scenario on the spot.
