Building AI Applications with Semantic Kernel
Eira Veyson
ISBN 13: 9798270453312
Published by Independently Published, 2025
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Synopsis:
Ship production ready AI apps in .NET with Semantic Kernel, agents, RAG, and Microsoft.Extensions.AI.
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.
"About this title" may belong to another edition of this title.
Bibliographic Details
Title: Building AI Applications with Semantic Kernel
Publisher: Independently Published
Publication Date: 2025
Binding: Paperback
Condition: New
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Building AI Applications with Semantic Kernel (Paperback)
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Paperback. Condition: new. Paperback. Ship production ready AI apps in .NET with Semantic Kernel, agents, RAG, and Microsoft.Extensions.AI.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 ServiceBootstrap a kernel with dependency injection, then stream chat responses with minimal latencySwitch providers at runtime, OpenAI, Azure OpenAI, GitHub Models, Azure AI Inference, and local OllamaDefine tools and argument schemas, validate inputs, and return structured outputs with typed resultsAdopt reliable planning, native function calling by default, plus Handlebars and Stepwise when they fitBuild agents with the SK Agent Framework for single agent and multi agent collaboration with tool routingAdd safety gates, Azure AI Content Safety, prompt shields, and failure handling in request pipelinesUse the Process Framework for deterministic workflows, events, steps, retries, and idempotencyDesign retrieval with Microsoft.Extensions.VectorData, schemas, metadata, filters, and hybrid searchStand up Azure AI Search vector indexes, ingestion, scoring profiles, and grounded RAG with citationsTune performance, top k, context sizing, freshness, and token budgets for predictable costImplement OpenTelemetry traces, logs, and metrics, plus usage tracking, alerts, and dashboardsRun continuous evaluation in CI with quality gates for prompts, tools, and retrieval changesShip with .NET Aspire or containers, manage environment config, secrets, and private networkingHandle rate limits with backoff, batching, streaming design, and per tenant quotasPrepare incident playbooks, degraded modes, kill switches, and safe rollback plansFollow end to end case studies, a chat assistant with tools, a multi agent workflow, and a RAG serviceThis 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9798270453312
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Building AI Applications with Semantic Kernel (Paperback)
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Paperback. Condition: new. Paperback. Ship production ready AI apps in .NET with Semantic Kernel, agents, RAG, and Microsoft.Extensions.AI.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 ServiceBootstrap a kernel with dependency injection, then stream chat responses with minimal latencySwitch providers at runtime, OpenAI, Azure OpenAI, GitHub Models, Azure AI Inference, and local OllamaDefine tools and argument schemas, validate inputs, and return structured outputs with typed resultsAdopt reliable planning, native function calling by default, plus Handlebars and Stepwise when they fitBuild agents with the SK Agent Framework for single agent and multi agent collaboration with tool routingAdd safety gates, Azure AI Content Safety, prompt shields, and failure handling in request pipelinesUse the Process Framework for deterministic workflows, events, steps, retries, and idempotencyDesign retrieval with Microsoft.Extensions.VectorData, schemas, metadata, filters, and hybrid searchStand up Azure AI Search vector indexes, ingestion, scoring profiles, and grounded RAG with citationsTune performance, top k, context sizing, freshness, and token budgets for predictable costImplement OpenTelemetry traces, logs, and metrics, plus usage tracking, alerts, and dashboardsRun continuous evaluation in CI with quality gates for prompts, tools, and retrieval changesShip with .NET Aspire or containers, manage environment config, secrets, and private networkingHandle rate limits with backoff, batching, streaming design, and per tenant quotasPrepare incident playbooks, degraded modes, kill switches, and safe rollback plansFollow end to end case studies, a chat assistant with tools, a multi agent workflow, and a RAG serviceThis 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9798270453312
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