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Course Outline
Introduction to GPT-5 and Developer Capabilities
- GPT-5 key capabilities, multi-modality, and agent features
- Choosing models, understanding pricing and limits
- Ethical considerations and enterprise governance
Prompting and System Design for Reliable Outputs
- Prompt patterns, system messages, and context engineering
- Chain-of-thought vs. concise prompting and few-shot techniques
- Testing prompts and establishing acceptance criteria
APIs, SDKs, and Local Dev Workflow
- Calling GPT-5 APIs, SDK usage, authentication and secrets management
- Local development, mocking responses, and sandboxing
- Versioning, request/response schemas, and error handling
Building Agents and Tool Integrations
- Designing safe agent architectures and tool interfaces
- Routing, orchestration, and fallback strategies
- Rate-limits, concurrency control, and transactional considerations
Testing, Evaluation and Validation
- Automated test suites for prompts and behaviors
- Red-teaming, fuzz testing, and adversarial examples
- Metrics for accuracy, hallucination rates, and user satisfaction
Deployment, Monitoring and Observability
- CI/CD patterns for model-enabled features and canary releases
- Logging, tracing, and telemetry for prompt-level observability
- Alerting, SLA considerations, and incident response
Security, Privacy and Cost Optimization
- Data handling, PI/PHI considerations, and context sanitization
- Access control, auditing, and compliance checkpoints
- Token usage optimization, batching, and caching strategies
Summary and Next Steps
Requirements
- An understanding of at least one programming language such as Python or JavaScript
- Experience calling REST APIs or SDKs
- Basic familiarity with ML/AI concepts and JSON data structures
Audience
- Software engineers
- ML engineers
- DevOps / SRE engineers
14 Hours
Testimonials (3)
I very much appreciated the way the trainer presented everything. I understood everything even if Finance is not my area, he made sure that every participant was on the same page, while keeping up with the time left. The exercises were placed at good intervals. Communication with the participants was always there. The material was perfect, not too much, not too little. He elaborated very well on a bit more complicated subjects so that it can be understood by everyone.
Diana
Course - ChatGPT for Finance
The subject matter and the way it was presented.
Mark Mackintosh - IUM Insurance
Course - ChatGPT for Executives
Practical examples in Insurance
