Generative AI for Developers Training in Madrid, Spain

The Generative AI for Developers Training is a hands-on program designed to teach developers how to build, integrate, and deploy Generative AI capabilities into modern software applications.

The course covers LLM APIs, prompt engineering, structured outputs, AI workflows, RAG, agents, testing, security, and production deployment, enabling developers to create scalable, reliable, and enterprise-grade AI-powered applications.

Introduction to Generative AI for Software Development

• Generative AI in modern software systems
• LLM capabilities and limitations
• Typical AI application architectures

LLM APIs & SDKs

• Overview of major LLM APIs
• Authentication and API setup
• Request/response patterns
• Error handling and retries

Prompt Engineering for Developers

• Instruction design for APIs
• Context injection strategies
• Prompt templates and versioning
• Reducing hallucinations

Structured Outputs & Validation

• JSON and schema-based outputs
• Output parsing and validation
• Handling malformed responses
• Reliability patterns

Building AI Features

• Text generation features
• Summarization and extraction
• Classification and tagging
• AI-assisted search

Multi-Step AI Workflows

• Chaining LLM calls
• Conditional logic and branching
• State management
• Workflow orchestration patterns

Retrieval-Augmented Generation (RAG)

• RAG architecture overview
• Document ingestion pipelines
• Vector databases (conceptual + examples)
• Grounded response generation

AI Agents & Tool Use

• Agent concepts for developers
• Tool calling and function execution
• Agent orchestration patterns
• Human-in-the-loop controls

Testing & Evaluation of AI Systems

• Unit testing AI logic
• Prompt regression testing
• Output quality evaluation
• Monitoring drift and failures

Performance & Cost Optimization

• Token usage optimization
• Caching strategies
• Latency reduction
• Cost monitoring and limits

Security, Privacy & Compliance

• Securing API keys and secrets
• Data protection in AI apps
• Prompt injection defense
• Governance and auditability

Deployment & Production Operations

• CI/CD for AI-powered apps
• Environment separation
• Monitoring and observability
• Versioning models and prompts

Hands-on Exercises

Summary and Conclusion