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Building AI Agents with Microsoft Semantic Kernel and RAG: Unlock Productivity and Precision with Retrieval-Augmented Generation Patterns
Paperback
Publisher: Independently Published
Published: Nov 10 2025
Pages: 170
Weight: 0.67
Height: 0.36 Width: 7.00 Depth: 10.00
Language: English
Are you trying to build AI systems that are reliable, context-aware, and ready for real work-but keep running into hallucinations, messy pipelines, and tools that don't quite fit together? Many teams feel this gap. They want agents that reason with precision, take meaningful actions, and consistently produce grounded answers. What they need is a practical way to combine Microsoft Semantic Kernel with Retrieval-Augmented Generation (RAG) to create agents that truly perform.
Building AI Agents with Microsoft Semantic Kernel and RAG delivers exactly that. This book gives you a complete, developer-friendly blueprint for architecting intelligent agents powered by enterprise-grade retrieval patterns. You'll learn how to connect language models with Azure AI Search, orchestrate multi-step workflows, manage context windows, structure prompts, and build agents that act with clarity and accuracy. Every concept is backed by real implementation guidance, clear explanations, and production-tested patterns.
You'll learn how to:
- Build robust agent workflows using Microsoft Semantic Kernel's orchestration tools
- Use RAG to support grounded, context-aware responses that reduce hallucinations
- Construct efficient indexing, chunking, and embedding pipelines with Azure AI Search
- Integrate retrieval, reasoning, and tool use into coherent multi-agent systems
- Optimize latency, scalability, cost, and token efficiency in real-world deployments
- Apply proven prompt engineering strategies for consistent and precise outputs
- Deploy agents in cloud, hybrid, and on-prem environments
- Monitor, test, and refine agents using practical evaluation techniques
Whether you're a developer, engineer, architect, or technical leader, this book gives you the guidance, patterns, and confidence to build AI agents that actually work in production-not just in demos.
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