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Integrating AI into Applications

Certificate of attendance from Spirit in Projects Foundation

Would you like to exploit the full potential of Large Language Models (LLMs) in practice and integrate them into your existing systems? If you would like to find out more about the different application scenarios and develop practical solutions together, then you can look forward to this training. From text classification to system integration, you will learn how to effectively integrate LLMs into your infrastructure, build modern RAG architectures, use function calling and develop your own solutions for your real-world problems.

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AI Expert

Ihre Lernziele

Gain practical experience with various LLM application scenarios
Implementation of state-of-the-art NLP solutions with current models
Integration of LLMs into existing system landscapes
Develop an understanding of the strengths and limitations of different model types
Build and optimize RAG (Retrieval-Augmented Generation) architectures
Use function calling and tool use for extended functionality
Be able to design and implement your own LLM-based solutions

Zielgruppe

AI Expert Software developer System Architect Software Architect and anyone who wants to work with artificial intelligence

Kursinhalt

1 1. Basics of LLM integration
  • Architectural patterns for LLM-based systems
  • REST APIs and microservices with LLMs
  • Scalable infrastructures for LLM applications
  • Various deployment options (cloud, on-premise, hybrid)
  • Multi-model strategies (GPT, Claude, Gemini, open source models)
  • Security and governance
2 2. Current LLM models and APIs (new: 2026)
  • OpenAI GPT: Multimodal capabilities and API integration
  • Anthropic Claude: Large-Token Context Window, Tool Use
  • Google Gemini: Multi-Million-Token Context, Hybrid Reasoning
  • Use open source models safely: Llama, Mistral, DeepSeek and others
  • Model selection and performance-to-cost ratio
  • Practical exercise: Comparison of different LLM APIs for use cases
3 3. RAG (Retrieval-Augmented Generation) architectures
  • Basics of RAG: Improving factual fidelity
  • Vector databases: Pinecone, Weaviate, Chroma, Qdrant
  • Embedding models and vector search
  • Chunking strategies and metadata management
  • RAG pipeline optimization
  • Hybrid approaches: RAG + fine-tuning
  • Practical exercise: Building a RAG system with a vector database
4 4. Function calling and tool use
  • Concept of Function Calling
  • Integration of external tools and APIs
  • Multi-tool orchestration
  • Error handling and fallback strategies
  • Security aspects when using tools
  • Practical exercise: Expand LLM with external functions
5 5. Text classification and sentiment analysis
  • Building classification pipelines
  • Feature extraction with transformer models
  • Integration into content management systems
  • Batch and real-time processing
  • Practical exercise: Integration of a sentiment analyzer into an existing web application
6 6. Information extraction and document processing
  • Multilingual NER systems
  • Integration with document processing pipelines
  • Connection to document management systems
  • Workflow integration
  • Practical exercise: Development of a document processing pipeline with LLM integration
7 7. Chatbots and dialogue systems
  • Integration with messaging platforms
  • Connection to CRM systems
  • Context management and session handling
  • Memory strategies for longer conversations
  • Monitoring and logging
  • Practical exercise: Integrating an LLM-based chatbot into a corporate platform
8 8. Development frameworks and tools
  • LangChain: Modular Workflows and Memory Handling
  • LlamaIndex: Specialization in RAG applications
  • Semantic Kernel (Microsoft): Enterprise integration
  • Hugging Face: Open Source Models and Transformers
  • Practical exercise: Application development with LangChain
9 9. Observability and monitoring
  • LangSmith for LLM debugging and tracing
  • Weights & Biases for Experiment Tracking
  • Logging of prompts and responses
  • Performance metrics and latency monitoring
  • Cost control and budgeting
  • Practical exercise: Monitoring setup for LLM applications
10 10. System integration and productive operation
  • API design and management
  • A/B testing and gradual rollout
  • Error handling and fallback strategies
  • Scaling and load balancing
  • Fine-tuning and adjustment methods
  • Practical exercise: Implementing a complete LLM service with monitoring and failover

Certification

For this training you will receive a certificate of participation from Spirit in Projects.

Mehr zu Zertifizierungen

Nach dem Kurs empfehlen wir

Fundamentals of Deep Learning
AI and Data Ethics in Projects
AI Testing (Testing of AI-based Systems)

Kurs auf einen Blick

  • Certification Certificate of attendance from Spirit in Projects
  • Level Foundation
  • Dauer 2 days
  • Preis EUR 1,490.-- excl. VAT
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