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From Problems to Patterns: Generative AI in .Net: Build Next-Level AI Applications, Agents, and Tools with Microsoft's Modern Ecosystem
Paperback
Series: From Problems to Patterns
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Publisher: Independently Published
Pages: 480
Weight: 2.43
Height: 0.97 Width: 8.50 Depth: 11.00
Language: English
From Problems to Patterns: Generative AI in .Net, Build Next-Level AI Applications, Agents, and Tools with Microsoft's Modern Ecosystem.
Unleash the power of Generative AI in your .NET projects with this comprehensive guide-designed for AI engineers, solution architects, .NET developers, and technical leaders ready to deliver production-ready intelligent features.
Start by demystifying core generative AI concepts, differentiating probabilistic programs from traditional software, and exploring the rapidly evolving Microsoft AI ecosystem. Lay your foundation with Microsoft.Extensions.AI, leveraging a unified programming model to write cloud-agnostic, provider-neutral code. Learn the Practical Patterns the Pros Use-End to End
Go beyond simple prompt/response cycles-this book teaches you four core interaction patterns: conversations, streaming, function calling, and middleware pipelines. Implement advanced prompt engineering, structured outputs, semantic search, and robust streaming for responsive, reliable AI-powered software in C#. Build Full-Stack Solutions-From RAG to Vision Apps
Dive into deep, code-heavy walkthroughs showing how to create industry-grade AI features. Learn retrieval-augmented generation (RAG) to ground answers in your data, construct ingestion and indexing pipelines, measure output quality, implement inline citations, and orchestrate multi-pattern applications blending search, summarization, and action. Explore local model deployment and multimodal AI-including vision and document intelligence-without ever leaving the .NET ecosystem. Develop Robust AI Agents with the Microsoft Agent Framework
Build, orchestrate, and deploy autonomous agents with Microsoft's official agent platform. Go from stateless chat clients to multi-agent, graph-based workflows with persistent sessions, middleware, cross-agent communication, and seamless tool integration. Migrate from Semantic Kernel and AutoGen confidently with robust guides. Implement and Deploy Standardized AI Tools with MCP
Fully master the Model Context Protocol (MCP)-the open standard for tool discovery and execution. Build your own servers/clients, expose .NET logic as discoverable tools, connect agents to Azure, publish to NuGet, and secure every layer for enterprise use. Production Practices for Real Teams
Ship with confidence using proven methods for observability, diagnostics, performance, cost control, resilience, and security-including prompt injection defense and Responsible AI. Deploy with Azure, implement effective testing strategies for non-deterministic systems, and ensure regulatory readiness at scale. Approach in the Book: What You'll Get:
* In-depth tutorials for every major feature (with full source code)
* Decision guides, best practices, and architectural diagrams
* Quick references, provider comparisons, and migration checklists
* Real-world solutions for security, resilience, and operations Hands-On: What You Will Build
* A semantic product search engine using embeddings and vector similarity
* A production RAG pipeline - ingestion, chunking, retrieval strategies, inline citations, and LLM-as-judge evaluation
* An expense report processor that reads photos of receipts with vision AI
* Autonomous agents with persistent sessions, tool approval flows, and graph-based multi-agent workflows
* MCP servers packaged as NuGet tools and deployed to Azure Container Apps
* A fully offline AI app powered by Ollama - no API key, no cloud, no data leaving your machine From Problems to Patterns: Generative AI in .NET is your essential blueprint for building, shipping, and operating intelligent, grounded, and responsible AI features the Microsoft way-all with modern .NET.
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