Building Intelligent Systems with LLMs: RAG, AI Agents, and Beyond: Building the Next Generation of AI-Powered Applications - Softcover

Baqar, Mohammad; Khanda, Rajat; Naqvi, Saba

 
9798195350215: Building Intelligent Systems with LLMs: RAG, AI Agents, and Beyond: Building the Next Generation of AI-Powered Applications

Inhaltsangabe

Key Features

  • Build and optimize production-grade RAG pipelines for factual, grounded AI outputs
  • Design single-agent and multi-agent architectures for complex enterprise workflows
  • Apply evaluation, safety, privacy, and governance frameworks to deploy trustworthy AI at scale

Book Description:

Building Intelligent Systems with LLMs: RAG, AI Agents, and Beyondis a practical guide for engineers, architects, researchers, and technical leaders who want to move from LLM demos to reliable, scalable products. It bridges core AI theory with real implementation practices, showing how modern intelligent systems are designed, evaluated, secured, and operated under real enterprise constraints.

The book covers the full journey from foundational model concepts to advanced system architecture. You will learn how to design and tune Retrieval-Augmented Generation (RAG) pipelines, build autonomous and multi-agent workflows, and implement robust evaluation methods for quality, grounding, hallucination control, and safety. It also provides practical guidance for integrating guardrails, privacy controls, compliance-aware patterns, and operational governance.

With deep technical chapters and hands-on lab-style projects, this book walks through building AI agents, enterprise assistants, production retrieval platforms, vector and hybrid search systems, full DevSecOps delivery pipelines, and LLM safety controls. By the end, you will have the architecture patterns and operational discipline needed to launch trustworthy AI systems in production.

What you will learn
  • Understand the evolution from early neural networks to modern large language models
  • Design and optimize RAG pipelines for enterprise use cases
  • Build single-agent and multi-agent systems for planning and execution
  • Evaluate AI outputs for grounding, quality, hallucination risk, and safety
  • Implement guardrails, privacy controls, and compliance-ready AI patterns
  • Integrate LLM systems with APIs, databases, and external tools
  • Operate AI platforms with DevSecOps, observability, release gates, and incident playbooks
Who this book is for

This book is for software engineers, AI/ML practitioners, solution architects, researchers, and technical leaders building real-world AI applications. It is ideal for teams moving from prototype to production and for professionals who need reliable, auditable, and scalable LLM systems. Basic familiarity with Python and Generative AI concepts is recommended.

Table of Contents

  1. Preface
  2. The Evolution of Intelligence: From Perceptrons to Large Language Models
  3. How LLMs Work: Transformers, Attention, and Context
  4. Training and Fine-Tuning LLMs
  5. Prompt Engineering and In-Context Learning
  6. Retrieval-Augmented Generation (RAG)
  7. What Are AI Agents?
  8. Memory, Planning, and Tool Use in Agentic Systems
  9. Prompt Engineering in the Age of AI Agents and Vibe Coding
  10. Evaluating and Testing AI Systems
  11. RAG in Production: Scaling, Performance, and Cost
  12. Guardrails, Safety, and Reliability in Production AI
  13. Operational Readiness and AI Governance
  14. Agent-Oriented Architecture
  15. Operational Excellence for AI Systems
  16. The Next Wave of RAG
  17. Autonomous and Self-Evolving AI Systems
  18. AI-Driven Socio-Technical Systems and Global Impact
  19. Building AI Agents: Hands-On Project
  20. Building RAG Systems: Hands-On Project
  21. Building an Enterprise AI Assistant: Hands-On Project
  22. Building Multi-Agent Workflows: Hands-On Project
  23. RAG + Multi-Agent Integration Project
  24. Advanced Vector and Hybrid Retrieval Project
  25. Full DevSecOps Pipeline for AI Project
  26. LLM Safety Engineering Project
  27. Capstone and Deployment Playbook

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