Prompting .NET for AI Application Development: Build Production-Ready AI Applications with .NET, C#, ASP.NET Core, Semantic Kernel, RAG, Blazor, .NET MAUI, and Generative AI

Key Features
● Get a free one-month digital subscription to http://www.avaskillshelf.com.
● Production prompt engineering inside typed .NET service boundaries with Semantic Kernel and Azure OpenAI.
● End-to-end RAG pipelines with output validation, guardrails, fallbacks, and cost and latency controls.
● Two complete production scenarios — an AI e-commerce platform and an intelligent enterprise dashboard.

Book Description
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 Developmenttreats 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.

What you will learn
● Design reliable prompts for real-world .NET application workflows within clear service boundaries.
● Structure maintainable AI services, typed contracts, and Semantic Kernel orchestration Layers.
● Integrate Semantic Kernel and Azure OpenAI model providers into ASP.NET Core, Blazor, and MAUI.
● Build grounded RAG workflows with secure enterprise data and production-ready retrieval patterns.
● Validate structured outputs, implement fallbacks, and control latency, cost, and safety.
● Deploy and scale observable AI features across .NET 10 application types with governance controls.

Table of Contents
1. Prompt-Driven Application Development in .NET
2. Setting up a Production-Ready AI Development Environment in .NET
3. Designing Effective Prompts for Real .NET Workflows
4. .NET Architecture Patterns for AI-Powered Applications
5. Working with Semantic Kernel in Production-Oriented .NET Solutions
6. Building the First End-to-End AI-Powered .NET Application
7. Retrieval-Augmented Generation in Production .NET Applications
8. Prompt Optimization, Reliability, and Control in .NET
9. Building, Deploying, and Scaling an AI-Powered E-Commerce Platform with .NET
10. Building an Intelligent Enterprise Dashboard in .NET
Index