GPT-5 for Developers Training in Copenhagen, Denmark

The GPT-5 for Developers Training is a hands-on, technical program designed to help developers build production-grade AI applications using GPT-5.
The course focuses on API integration, prompt engineering, structured outputs, tool usage, agent workflows, scalability, security, and real-world application patterns, enabling teams to design reliable, scalable, and maintainable AI-powered systems.

This training is ideal for backend, frontend, full-stack, and platform engineers building AI features into web, mobile, and enterprise applications.

Introduction to GPT-5 for Developers

• Overview of GPT-5 capabilities and positioning

• Differences from previous GPT models

• Common developer use cases

• Architecture of GPT-powered applications

GPT-5 API Fundamentals

• Authentication and API setup

• Request/response patterns

• Streaming vs non-streaming responses

• Error handling basics

Prompt Engineering for Developers

• System, user, and developer instructions

• Prompt templates and reuse

• Controlling tone, format, and behavior

• Reducing hallucinations and improving reliability

Structured Outputs & Validation

• JSON and schema-based outputs

• Enforcing response formats

• Validating and parsing model outputs

• Handling malformed responses

Tool Calling & Function Integration

• Connecting GPT-5 to tools and functions

• API and database access via tools

• Controlled tool execution

• Secure tool usage patterns

Building AI Agents with GPT-5

• Agent concepts and responsibilities

• Task-based vs goal-based agents

• Memory and context management

• Human-in-the-loop agent designs

Multi-Step AI Workflows

• Chained prompts and pipelines

• Conditional logic and branching

• Orchestrating complex tasks

• Workflow state management

RAG (Retrieval-Augmented Generation)

• Connecting GPT-5 to knowledge bases

• Vector search and document retrieval

• Grounding responses with enterprise data

• Reducing hallucinations with retrieval

Performance & Cost Optimization

• Token and context optimization

• Caching and reuse strategies

• Latency reduction techniques

• Cost monitoring and control

Security, Privacy & Compliance

• Handling sensitive data

• Access control and secrets management

• Preventing prompt injection

• Audit logging and governance

Testing & Quality Assurance for AI Apps

• Unit testing AI components

• Regression testing for prompts

• Output quality and consistency checks

• Monitoring model behavior

Observability & Monitoring

• Logging prompts and responses

• Tracing multi-step workflows

• Detecting failures and anomalies

• Production troubleshooting

Deployment & Production Operations

• Deploying GPT-5 apps

• CI/CD for AI features

• Versioning prompts and workflows

• Rollouts and rollback strategies

Hands-on Exercises

Summary and Conclusion