LLM Agent Development using Semantic Kernel

Gebruik Semantic Kernel in C# om veilige, schaalbare AI agents te onwikkelen, inclusief testing en monitoring.

Beschrijving

In deze training leer je hoe je Semantic Kernel gebruikt om professionele LLM Agents in te zetten. Met veel praktische oefeningen maak je kennis met het volledige traject van het ontwikkelen van LLM Agents, waaronder onderwerpen als LLMOps, prompt templates, API-integraties, Retrieval Augmented Generation en geheugenbeheer.

Aan het einde van deze training ben je in staat om veilige en schaalbare AI-agent systemen te ontwerpen en ontwikkelen met Semantic Kernel, inclusief uitgebreide test- en monitoringmogelijkheden voor zakelijke toepassingen.

Leerdoelen

• Understand large language models, their capabilities, and how to select the right model for different use cases [Understand]
• Construct and configure Semantic Kernel applications with multiple AI connectors in both console and web environments [Apply]
• Write effective prompts using templates, hyperparameters, and few-shot learning techniques [Apply]
• Explain the need for LLMOps practices including testing, monitoring, cost management, and security measures [Understand]
• Produce chat-based applications using conversation history and streaming responses [Apply]
• Implement testing and monitoring for LLM applications using OpenTelemetry and standard unit-testing tools. [Apply]
• Construct custom tools and functions that extend LLM capabilities, including external API integrations and filters [Apply]
• Implement Retrieval Augmented Generation (RAG) systems with vector stores for knowledge-enhanced AI applications [Apply]
• Employ structured output generation using JSON formatting and sideband communication patterns [Apply]
• Produce production-ready AI agents with proper tool integration, memory management, and security constraints [Apply]

Onderwerpen

1. Introduction to Large Language Models
2. Configuring Semantic Kernel Applications
3. Effective prompting strategies
4. LLMOps
5. Building a chat-based application
6. Implementing testing and monitoring using OpenTelemetry
7. Extending LLM capabilities
8. Retrieval Augmented Generation (RAG) systems and vector stores
9. Structured output generation
10. Production-ready AI agents