GPT-5 for Developers Training Course
GPT-5 for Developers is a practical, hands-on training program designed to teach engineers how to integrate, extend, and operate GPT-5 safely and efficiently in production environments.
This instructor-led, live training (available both online and on-site) is targeted at intermediate-level developers who aim to develop production-grade applications, agents, and developer tools powered by GPT-5.
By the end of this training, participants will be able to:
- Design and implement robust GPT-5 API integrations using best practices for prompts and system instructions.
- Build and test tool-enabled agents and orchestrations that safely interact with external services.
- Set up testing, monitoring, and deployment workflows for features powered by GPT-5.
- Implement security, privacy, and cost-optimization strategies for enterprise deployments.
Format of the Course
- Interactive lectures and discussions.
- Hands-on coding labs and guided exercises.
- Team-based capstone mini-project.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
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 vs. concise prompting and few-shot techniques
- Testing prompts and establishing 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 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 behaviors
- 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 Optimization
- Data handling, PI/PHI considerations, and context sanitization
- Access control, auditing, and compliance checkpoints
- Token usage optimization, batching, and caching strategies
Summary and Next Steps
Requirements
- An understanding of at least one programming language such as Python or JavaScript
- Experience calling REST APIs or SDKs
- Basic familiarity with ML/AI concepts and JSON data structures
Audience
- Software engineers
- ML engineers
- DevOps / SRE engineers
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Testimonials (1)
The trainer provided clear explanations, and the topic is highly relevant
Madalina Spanu
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