Content:

– Introduction to LLMs and Spring AI:
... - Fundamentals of Artificial Intelligence and Language Models
... - Overview of the Spring AI Framework
– Basics of AI Interaction:
... - Understanding prompts and output parsing
... - First steps with chat models
... - Processing structured responses
– Prompt Techniques and Advanced Concepts:
... - Prompt Engineering Best Practices
... - Stuffing, chaining, and structuring prompts
... - Optimizing model interaction
– Retrieval Augmented Generation (RAG):
... - Using your own data with LLMs
... - Vector databases and embeddings
... - Implementing RAG patterns
– Function Calls and Structured Results:
... - APIs and system integration
... - Function and tool calling with Spring AI
... - MCP Clients and Servers
– Media Integration:
... - Image generation with AI models
... - Audio transcription and speech processing
... - Developing multimodal applications
– Advanced Practices:
... - Evaluation and quality assurance
... - Observability and monitoring
... - Chat Memory and guardrails
– Multi-Model Approaches:
... - Combining local and cloud models
... - Model selection and optimization
... - Cost-effective AI strategies
– Agentic Patterns:
... - AI-powered agents for complex tasks
... - Workflow automation with AI
... - Developing autonomous systems
– Outlook and Best Practices:
... - Trends in AI development
... - Operating productive AI applications

We focus on deepening and understanding a specific selection of topics.

Disclaimer: The actual course content may vary from the above, depending on the trainer, implementation, duration and constellation of participants.

Whether we call it training, course, workshop or seminar, we want to pick up participants at their point and equip them with the necessary practical knowledge so that they can apply the technology directly after the training and deepen it independently.