SKU/Artículo: AMZ-B0FJ8QQ4CY
Building effective LLM-based applications with Semantic Kernel
Format:
Kindle
Detalles del producto
Disponibilidad:
En stock
En stock
Peso con empaque:
0.15 kg
0.15 kg
Devolución:
Sí
Sí
Condición
Nuevo
Nuevo
Producto de:
Amazon
Amazon
Viaja desde
USA
USA
Sobre este producto
- Learn how to build valuable LLM-based applications in C# Want to build real applications with Large Language Models but are 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 the author's trial-and-error experiences, he walks you through proven patterns and workflows that work, from basic prompt engineering to building agents.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 provides the concrete patterns and battle-tested approaches you need to succeed with LLMs.About the technologyWorking with different LLM providers and designing around brittle API endpoints are challenging problems on their own. Semantic Kernel provides a way to abstract away from the models and API endpoints and start thinking about smart application building blocks. In this book, you learn useful patterns and practices to get the most out of Large Language Models without solving all the challenging low-level problems.Who this book is forThis book is for C# developers and software architects who want to use an LLM in their application to solve specific challenges that can't be solved with normal program logic. Table of contentsUnderstanding Large Language ModelsEssential LLMOps knowledgeGetting Started with Semantic KernelThe art and nonsense of prompt engineeringTesting and monitoring promptsEnhancing LLMs with toolsRetrieval augmented generationWorking with structured outputPrompt chaining workflowsIntelligent Request Routing WorkflowsWorking with agents
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