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Duration 14 hours (2 days)
Course Outline
Overview of GPT-5 and Its Developer Applications
- Core GPT-5 capabilities, multi-modal features, and agent functionalities.
- Model selection strategies, along with an understanding of pricing structures and usage limits.
- Ethical frameworks and enterprise governance principles.
Prompting Strategies and System Design for Reliability
- Effective prompt patterns, system message usage, and context engineering.
- Comparing chain-of-thought reasoning with concise prompting and few-shot learning techniques.
- Prompt testing methodologies and defining clear acceptance criteria.
APIs, SDKs, and Local Development Workflows
- Interacting with GPT-5 APIs, utilizing SDKs, and managing authentication and secrets.
- Local development practices, including response mocking and sandboxing.
- Version control, request/response schema design, and error management.
Constructing Agents and Tool Integrations
- Architecting secure agent structures and tool interfaces.
- Implementing routing, orchestration logic, and fallback mechanisms.
- Managing rate limits, concurrency, and transactional integrity.
Testing, Evaluation, and Validation Methods
- Developing automated test suites for prompts and system behaviors.
- Conducting red-teaming, fuzz testing, and adversarial example analysis.
- Establishing metrics for accuracy, hallucination rates, and user satisfaction.
Deployment, Monitoring, and Observability Practices
- CI/CD patterns for model-driven features and canary release strategies.
- Implementing logging, tracing, and telemetry for prompt-level visibility.
- Setting up alerts, SLA management, and incident response protocols.
Security, Privacy, and Cost Optimization
- Data management practices, considerations for PI/PHI, and context sanitization.
- Access control mechanisms, auditing procedures, and compliance verification.
- Optimizing token usage, implementing batching, and applying caching strategies.
Conclusion and Future Directions
Requirements
- Familiarity with at least one programming language, such as Python or JavaScript.
- Experience interacting with REST APIs or utilizing SDKs.
- Foundational knowledge of ML/AI concepts and JSON data structures.
Target Audience
- Software Engineers
- ML Engineers
- DevOps and SRE Engineers
Testimonials (1)
Able to pivot upon audience suggestions - ie able to create a real AI agent scenario on the spot.
