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 Duration 14 hours

Course Outline

Introduction to GPT-5 and Developer Capabilities

  • Key GPT-5 capabilities, multi-modality, and agent features
  • Selecting models, understanding pricing structures and limitations
  • 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 defining 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 secure 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

  • Proficiency in at least one programming language, such as Python or JavaScript
  • Experience in calling REST APIs or utilizing SDKs
  • Basic familiarity with ML/AI concepts and JSON data structures

Target Audience

  • Software engineers
  • ML engineers
  • DevOps / SRE engineers

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