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Duration 14 hours
Course Outline
Foundations of GPT-5 and Developer Capabilities
- Exploring key GPT-5 capabilities, multi-modality, and agent features.
- Strategies for selecting models, and understanding pricing structures and usage limits.
- Ethical frameworks and enterprise governance standards.
Prompt Engineering and System Design for Consistent Results
- Mastering prompt patterns, system messages, and context engineering.
- Comparing chain-of-thought reasoning with concise prompting and few-shot techniques.
- Validating prompts and defining clear acceptance criteria.
APIs, SDKs, and Local Development Environments
- Integrating with GPT-5 APIs, utilizing SDKs, and managing authentication and secrets securely.
- Techniques for local development, mocking responses, and sandboxing environments.
- Managing versioning, request/response schemas, and robust error handling.
Creating Agents and Integrating Tools
- Designing secure agent architectures and defining tool interfaces.
- Implementing routing, orchestration, and fallback mechanisms.
- Addressing rate limits, concurrency control, and transactional integrity.
Testing, Evaluation, and Validation Practices
- Building automated test suites for prompt behavior and outputs.
- Applying red-teaming, fuzz testing, and adversarial example analysis.
- Defining metrics for accuracy, hallucination rates, and user satisfaction.
Deployment, Monitoring, and Observability
- Implementing CI/CD patterns for model-enabled features and executing canary releases.
- Utilizing logging, tracing, and telemetry for granular prompt-level observability.
- Setting up alerting systems, considering SLAs, and planning incident response.
Security, Privacy, and Cost Efficiency
- Managing data handling, PI/PHI compliance, and context sanitization.
- Implementing access controls, auditing processes, and compliance checkpoints.
- Optimizing token usage through batching and caching strategies.
Conclusions and Future Directions
Requirements
- Proficiency in at least one programming language, such as Python or JavaScript.
- Practical experience with calling REST APIs or utilizing SDKs.
- Foundational knowledge of ML/AI concepts and JSON data structures.
Target Audience
- Software engineers
- Machine Learning engineers
- DevOps and SRE engineers
Testimonials (2)
correct way of prompting and including guardrails in instructions.
YEO SHI MIN - ST Engineering Aerospace Ltd
Course - ChatGPT and Microsoft 365 Copilot for Advanced Productivity
Able to pivot upon audience suggestions - ie able to create a real AI agent scenario on the spot.
