About This Course

The Future of .NET Development Is Not AI Integration. It is AI Engineering. Most .NET developers can call an AI, API. Very few can build AI features that hold up in production. Prompting .NET for AI Application Development treats prompting as engineering — something that belongs inside service boundaries, typed contracts, secure configuration, and observable workflows, not inside a demo that ships once, and breaks quietly. Starting from prompt design and environment setup, the book builds progressively through .NET architecture patterns, Semantic Kernel orchestration, structured outputs, and integration across ASP.NET Core, Blazor, and .NET MAUI. RAG pipelines, output validation, guardrails, cost controls, and deployment follow — each grounded in practical .NET code and real implementation decisions. Two complete production scenarios bring it together: An AI-powered e-commerce platform and an intelligent enterprise dashboard. By the end of the book, you will build, harden, and operate .NET AI features that remain reliable when providers fail, inputs change, and applications scale.

About Your Author

Raúl Daniel Pimienta Ramos is a software developer and international author with more than nine years of experience building mobile, web, and backend solutions within the Microsoft ecosystem. He specializes in cross-platform application development using .NET MAUI and Xamarin.Forms, as well as ASP.NET Core, Blazor, C#, SQL Server, and Microsoft Azure. Pedro Luis López Gaitán is a software developer specializing in .NET technologies, web development, and mobile applications. He has in-depth experience with C#, .NET MAUI, React, Angular, Python, and PHP, as well as cloud solutions. He is the co-author of Ultimate .NET MAUI Projects and is passionate about creating innovative solutions.

What You'll Learn

12 Chapters · 12 Lessons

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      Prompt-Driven Application Development in .NET

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      Setting up a Production-Ready AI Development Environment in .NET

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      Designing Effective Prompts for Real .NET Workflows

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      .NET Architecture Patterns for AI-Powered Applications

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      Working with Semantic Kernel in Production-Oriented .NET Solutions

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      Building the First End-to-End AI-Powered .NET Application

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      Retrieval-Augmented Generation in Production .NET Applications

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      Prompt Optimization, Reliability, and Control in .NET

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      Building, Deploying, and Scaling an AI-Powered E-Commerce Platform with .NET

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      Building an Intelligent Enterprise Dashboard in .NET

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      Index