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

Key FeaturesBuild and optimize production-grade RAG pipelines for factual, grounded AI outputsDesign single-agent and multi-agent architectures for complex enterprise workflowsApply evaluation, safety, privacy, and governance frameworks to deploy trustworthy AI at scaleBook 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 learnUnderstand the evolution from early neural networks to modern large language modelsDesign and optimize RAG pipelines for enterprise use casesBuild single-agent and multi-agent systems for planning and executionEvaluate AI outputs for grounding, quality, hallucination risk, and safetyImplement guardrails, privacy controls, and compliance-ready AI patternsIntegrate LLM systems with APIs, databases, and external toolsOperate AI platforms with DevSecOps, observability, release gates, and incident playbooksWho this book is forThis 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 ContentsPrefaceThe Evolution of Intelligence: From Perceptrons to Large Language ModelsHow LLMs Work: Transformers, Attention, and ContextTraining and Fine-Tuning LLMsPrompt Engineering and In-Context LearningRetrieval-Augmented Generation (RAG)What Are AI Agents?Memory, Planning, and Tool Use in Agentic SystemsPrompt Engineering in the Age of AI Agents and Vibe CodingEvaluating and Testing AI SystemsRAG in Production: Scaling, Performance, and CostGuardrails, Safety, and Reliability in Production AIOperational Readiness and AI GovernanceAgent-Oriented ArchitectureOperational Excellence for AI SystemsThe Next Wave of RAGAutonomous and Self-Evolving AI SystemsAI-Driven Socio-Technical Systems and Global ImpactBuilding AI Agents: Hands-On ProjectBuilding RAG Systems: Hands-On ProjectBuilding an Enterprise AI Assistant: Hands-On ProjectBuilding Multi-Agent Workflows: Hands-On ProjectRAG + Multi-Agent Integration ProjectAdvanced Vector and Hybrid Retrieval ProjectFull DevSecOps Pipeline for AI ProjectLLM Safety Engineering ProjectCapstone and Deployment Playbook Read more