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Course Outline
Introduction to GPT-5 and Developer Capabilities
- Key capabilities of GPT-5, including multi-modality and agent features
- Selecting the right model and understanding pricing structures and limits
- Ethical considerations and enterprise governance frameworks
Prompting and System Design for Reliable Outputs
- Prompt patterns, system messages, and context engineering techniques
- Chain-of-thought reasoning versus concise prompting and few-shot methods
- Testing prompts and defining clear acceptance criteria
APIs, SDKs, and Local Dev Workflow
- Invoking GPT-5 APIs, utilizing SDKs, and managing authentication and secrets
- Local development practices, response mocking, and sandboxing
- Version control, request/response schemas, and robust error handling
Building Agents and Tool Integrations
- Designing secure agent architectures and tool interfaces
- Routing logic, orchestration, and fallback mechanisms
- Managing rate limits, concurrency control, and transactional integrity
Testing, Evaluation and Validation
- Creating automated test suites for prompts and behavioral outputs
- Red-teaming, fuzz testing, and analyzing adversarial examples
- Measuring accuracy, hallucination rates, and user satisfaction metrics
Deployment, Monitoring and Observability
- CI/CD patterns for model-enabled features and canary release strategies
- Implementing logging, tracing, and telemetry for prompt-level observability
- Setting up alerting, defining SLAs, and planning incident response
Security, Privacy and Cost Optimization
- Data handling protocols, PII/PHI considerations, and context sanitization
- Access control measures, auditing trails, and compliance checkpoints
- Optimizing token usage, implementing batching, and caching strategies
Summary and Next Steps
Requirements
- Proficiency in at least one programming language, such as Python or JavaScript
- Practical experience in interacting with REST APIs or SDKs
- Foundational knowledge of ML/AI concepts and JSON data structures
Target Audience
- Software engineers
- Machine Learning 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.
