Building effective LLM-based applications with Semantic Kernel
Building effective LLM-based applications with Semantic Kernel
About the Book
Want to build real applications with Large Language Models but tired of sifting through theoretical explanations? This hands-on guide shows you exactly how to integrate LLMs into production applications using Semantic Kernel and C#.
Drawing from my own trial-and-error experiences, I'll walk you through proven patterns and workflows that actually work—from basic prompt engineering to complex multi-agent systems.
You'll learn how to enhance LLMs with external tools, implement RAG for grounding responses in your data, and orchestrate sophisticated AI workflows. Through practical examples and case studies, you'll master the essential techniques for building reliable, scalable AI applications that solve real business problems.
Whether you're adding AI capabilities to existing enterprise software or building new AI-native applications, this book gives you the concrete patterns and battle-tested approaches you need to succeed with LLMs.
About the Author
Table of Contents
- Preface
- Why I wrote this book
- Who this book is for
- What this book covers
- System requirements
- Running the sample code in this book
- Feedback and issues
- Understanding Large Language Models
- What are LLMs and why they matter
- My journey with LLMs
- The current LLM landscape
- Key concepts and terminology
- Practical considerations for working with LLMs
- Real-world applications and use case
- Summary
- Further reading
- Essential LLMOps knowledge
- What is LLMOps?
- Testing LLM-based applications
- Monitoring and evaluation of your application
- Cost management and optimization
- Dealing with rate limits and capacity planning
- LLM performance and the user experience of your application
- Failover strategies
- Security and privacy in an LLM-based application
- Considerations when looking for tools
- Summary
- Further Reading
- Getting Started with Semantic Kernel
- Understanding Semantic Kernel
- Setting up your development environment
- Setting up a project with Semantic Kernel
- Executing your first prompt with Semantic Kernel
- Running the examples
- Summary
- The art and nonsense of prompt engineering
- Why are prompts important for effective LLM-Based applications?
- The 5 basics of a good prompt
- Writing prompt templates for reusability
- Using the chat history to your advantage
- Security considerations when using prompts
- Summary
- Testing and monitoring prompts
- Establishing a good test strategy for prompts
- Using deterministic testing methods to validate prompts
- Using model-based testing methods to validate prompts
- The dangers of the model-based testing approach
- Monitoring prompt interactions in production
- Summary
- Further reading
- Enhancing LLMs with tools
- What are tools, skills, and plugins
- When and where to use tools
- Building tools using a kernel function
- Sharing functions across applications with OpenAPI
- Applying filters to functions
- Summary
- Preface
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