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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 versus concise prompting and few-shot techniques
- Testing prompts and establishing acceptance criteria
APIs, SDKs, and Local Development 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 behaviours
- 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 Optimisation
- Data handling, PI/PHI considerations, and context sanitisation
- Access control, auditing, and compliance checkpoints
- Token usage optimisation, batching, and caching strategies
Summary and Next Steps
Requirements
- A working understanding of at least one programming language, such as Python or JavaScript
- Experience calling REST APIs or using SDKs
- Basic familiarity with ML/AI concepts and JSON data structures
Audience
- Software engineers
- ML engineers
- DevOps / SRE engineers
14 Hours
Testimonials (1)
Able to pivot upon audience suggestions - ie able to create a real AI agent scenario on the spot.
