Many teams can prototype an LLM feature, fewer can ship one that is observable, cost aware, and easy to maintain. Model choices, tool calling, retrieval, safety, and deployment details often derail real projects.

This book gives .NET developers a clear path from first chat to production. You will use provider neutral abstractions, add structured tools, integrate retrieval and memory, and deploy with confidence on Azure or containers.

Set up clean architecture with Semantic Kernel, Microsoft.Extensions.AI, and Azure AI Agent Service
Bootstrap a kernel with dependency injection, then stream chat responses with minimal latency
Switch providers at runtime, OpenAI, Azure OpenAI, GitHub Models, Azure AI Inference, and local Ollama
Define tools and argument schemas, validate inputs, and return structured outputs with typed results
Adopt reliable planning, native function calling by default, plus Handlebars and Stepwise when they fit
Build agents with the SK Agent Framework for single agent and multi agent collaboration with tool routing
Add safety gates, Azure AI Content Safety, prompt shields, and failure handling in request pipelines
Use the Process Framework for deterministic workflows, events, steps, retries, and idempotency
Design retrieval with Microsoft.Extensions.VectorData, schemas, metadata, filters, and hybrid search
Stand up Azure AI Search vector indexes, ingestion, scoring profiles, and grounded RAG with citations
Tune performance, top k, context sizing, freshness, and token budgets for predictable cost
Implement OpenTelemetry traces, logs, and metrics, plus usage tracking, alerts, and dashboards
Run continuous evaluation in CI with quality gates for prompts, tools, and retrieval changes
Ship with .NET Aspire or containers, manage environment config, secrets, and private networking
Handle rate limits with backoff, batching, streaming design, and per tenant quotas
Prepare incident playbooks, degraded modes, kill switches, and safe rollback plans
Follow end to end case studies, a chat assistant with tools, a multi agent workflow, and a RAG service

This is a code heavy guide with working C#, Bash, YAML, JSON, SQL, and configuration snippets that map directly to real services.

Grab your copy today and ship AI features your team can operate with confidence.

"Synopsis" may belong to another edition of this title.

PBShop.store US

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We first started out as “The Paperback Exchange,” a chain of physical bookstores where we would part exchange your beloved books for new stories to transport you to faraway places. However, as shopping started to evolve to online shops and marketplaces, we bid our stores goodbye to become “PBShop.” This transition has only allowed us to blossom as we now ship thousands of titles to book lovers across the globe. We pride ourselves in being a community of local book lovers which allows our passion and devotion to shine in everything we do. In 2020 we not only celebrated our 20th birthday but our 1st birthday as being completely employee owned after becoming an E.O.T in September 2019. We are proud to be different and embrace standing apart on a book mountain by working from a virtual inventory which allows us to provide thousands of books that may be difficult to get for your bookshelf or your studies. Working with a number of different suppliers allows us to explore other avenues such as puzzles, sheet music and even stationery so we really do have something for everyone. Life is about being versatile in all realms of existence. If this is your first visit or you are a returning customer, we would like to welcome you to the PBShop family, for there is no friend as loyal as a book. We are a company who put our customers at the centre of everything we do as we understand the importance of reading because once you learn to read, you will forever be free. There are a whole lot of things in this world of ours that we are yet to explore, which is why we will forever inspire curious minds.…

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Building AI Applications with Semantic Kernel

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