Prompt Engineering for Developers Training in Copenhagen, Denmark

The Prompt Engineering for Developers Training is a hands-on program designed to teach developers how to design, test, and optimize prompts for LLM-powered applications.

The course focuses on API-based prompting, structured outputs, prompt versioning, evaluation, and production reliability, enabling teams to build consistent, scalable, and testable AI features.

Prompt Engineering in Software Systems

• Why prompts are application logic
• Prompting vs traditional programming
• Common prompt failure modes
• Reliability challenges in production

LLM API Prompting Fundamentals

• System, user, and developer messages
• Instruction hierarchy
• Context window management
• Token and prompt size considerations

Designing Clear & Deterministic Prompts

• Task, context, and constraints pattern
• Explicit output format control
• Tone and style constraints
• Reducing ambiguity

Few-Shot & Example-Based Prompting

• When and how to use examples
• Designing high-quality examples
• Avoiding example bias
• Scaling few-shot templates

Structured Outputs & Schema Enforcement

• JSON and schema prompts
• Output validation strategies
• Handling malformed outputs
• Retrying and repair patterns

Prompt Chaining & Multi-Step Workflows

• Breaking tasks into steps
• Passing context between prompts
• Conditional logic
• Workflow orchestration patterns

Reducing Hallucinations & Errors

• Grounding with data
• Asking for uncertainty flags
• Verification and cross-check prompts
• Fallback strategies

Prompt Testing & Evaluation

• Unit testing prompts
• Prompt regression testing
• Quality metrics and evaluation sets
• A/B testing prompts

Prompt Versioning & Lifecycle Management

• Storing and managing prompt versions
• Change control and approvals
• Rollback strategies
• Documentation and audit trails

Prompt Security & Safety

• Prompt injection risks
• Input sanitization
• Output filtering
• Guardrails and policy prompts

Performance & Cost Optimization

• Token minimization
• Prompt compression strategies
• Caching and reuse
• Latency optimization

Prompting for Common Developer Tasks

• Extraction and classification prompts
• Summarization and transformation
• Code explanation and generation (support)
• Data cleaning and normalization

Prompting with Tools & Functions

• Function calling and tool use prompts
• Designing tool schemas
• Tool selection strategies
• Handling tool errors

Prompting for RAG & Knowledge Systems

• Prompting with retrieved context
• Citation and grounding prompts
• Chunking and context assembly
• Reducing retrieval noise

Production Best Practices

• Observability for prompts
• Monitoring output quality
• Drift detection
• Continuous improvement

Hands-on Exercises

Summary and Conclusion