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Course Outline
Introduction to GPT-5 and Developer Capabilities
- Key capabilities of GPT-5, including multi-modality and agent features.
- Model selection, understanding pricing structures, and usage limits.
- Ethical considerations and enterprise governance.
Prompting and System Design for Reliable Outputs
- Prompt patterns, system messages, and context engineering.
- Comparing chain-of-thought with concise prompting and few-shot techniques.
- Testing prompts and establishing acceptance criteria.
APIs, SDKs, and Local Development Workflow
- Calling GPT-5 APIs, using SDKs, and managing authentication and secrets.
- Local development setup, mocking responses, and sandboxing.
- Versioning, request/response schemas, and error handling.
Building Agents and Tool Integrations
- Designing secure agent architectures and tool interfaces.
- Routing, orchestration, and fallback strategies.
- Managing rate-limits, concurrency control, and transactional considerations.
Testing, Evaluation, and Validation
- Developing automated test suites for prompts and behaviors.
- Red-teaming, fuzz testing, and handling adversarial examples.
- Evaluating 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 mechanisms, SLA considerations, and incident response.
Security, Privacy, and Cost Optimization
- Data handling, PI/PHI considerations, and context sanitization.
- Access control, auditing, and compliance checkpoints.
- Optimizing token usage, batching, and caching strategies.
Summary and Next Steps
Requirements
- Proficiency in at least one programming language, such as Python or JavaScript.
- Experience working with 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 (1)
Able to pivot upon audience suggestions - ie able to create a real AI agent scenario on the spot.
