Shahani R. - Building Reliable AI Systems [2026, PDF, ENG] torrent

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Building Reliable AI Systems

Год издания: 2026
Автор: Shahani R.

Издательство: Manning
ISBN: 978-1633436732
Язык: Английский

Формат: PDF
Качество: Издательский макет или текст (eBook)
Интерактивное оглавление: Да
Количество страниц: 368

Описание: Building Reliable AI Systems: Applications and agents you can trust shows you how to grow a promising prototype into a production product you can deliver, maintain, and scale. In 11 sharply-focused chapters, it walks you through a unique reliability process that author Rush Shahani refined while building an AI-powered sales intelligence platform used by over 15,000 go-to-market teams. Timely and practical, this book provides concrete steps to eliminate costly hallucinations, streamline computational resources, and confidently deploy secure, trustworthy, and compliant AI solutions.
You’ll immediately appreciate how Building Reliable AI Systems defines “AI reliability” along six critical dimensions—accuracy and grounding, safe agency, graceful failure, consistency, fairness, and operational efficiency—and provides a specific three-layer framework to achieve these goals. The first layer, with a focus on outputs, shows you how to get consistent and accurate responses using well-engineered prompts, RAG, and model customization. The second layer, about agents, establishes parameters for memory, tool usage, and orchestration in agentic systems. The book concludes with the third layer—Reliable Operations—covering deployment, monitoring, and responsible AI.
The book’s reliability framework emphasizes the symbiotic relationship between evaluation and reliability, proving that you cannot improve what you do not measure. You’ll learn to measure your applications using fine-grained LLM-native evaluation rubrics like the Grounding Defect Rate, Hallucination Severity Score, and FActScore that audit everything from RAG outputs to the entire step-by-step reasoning trajectory of autonomous agents.
Building Reliable AI Systems stays rooted in reality from start to finish. As you go, you will build several projects, including a multi-agent travel planner and a medical assistant, and use industry standard tools and specifications like LangGraph and MCP. In the helpful appendices you’ll find a handy reference architecture and decision-making checklists.
Reviewer Mohamed Zohir Koufi, Lead AI Engineer at Capgemini, praises how the book “brings together architecture, tooling, evaluation, and governance in the way real systems are built.” By taking this comprehensive approach to LLMOps, the book delivers a reproducible process to ensure your applications stay cost-effective, fast, and compliant with enterprise standards like HIPAA and GDPR.
What's inside
- Ground outputs in real business data
- Orchestrate safe, consistent multi-step agent workflows
- Implement robust monitoring, semantic caching, and multi-model fallbacks
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[spoiler="Оглавление"]
preface xii
acknowledgments xiv
about this book xvi
about the author xix
about the cover illustration xx
1 AI reliability: Building LLMs for the real world 1
PART 1 RELIABLE OUTPUTS 15
2 Generating trustworthy responses with prompt engineering 17
3 Grounding outputs with RAG 47
4 Embeddings and vector search 85
5 Fine-tuning LLMs for improved performance 126
PART 2 RELIABLE AGENTS 153
6 Creating effective AI agents 155
7 Tool integration and MCP 184
8 Multi-agent systems 205
PART 3 RELIABLE OPERATIONS 235
9 Evaluation and performance for LLMs and agents 237
10 Deploying and monitoring 273
11 Bias, privacy, and responsible AI 299
references 331
bibliography 335
index 339
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