Overview

Foundation models are redefining how modern software is built. From intelligent search and automation to multi-agent systems and enterprise copilots, the era of AI-powered engineering has arrived. But turning powerful models into reliable, scalable systems requires more than prompts. It requires real engineering. This book shows you how. This practical guide teaches you how to design, build, and maintain production-grade AI systems using foundation models. Instead of chasing the latest model release, you will learn durable principles, proven patterns, and reliable practices for creating robust, testable, and scalable AI applications. Whether you are building retrieval-augmented systems, agent workflows, or domain-specific intelligent tools, this book gives you the engineering foundation you need to succeed. Inside, you will explore how to integrate foundation models into real-world systems with clarity and confidence. You will learn how to design data pipelines, vector stores, retrieval layers, tool-based reasoning, and agentic coordination. You will see how to evaluate model quality, monitor behaviors, secure your system, and scale to production environments. You will also discover how to apply architectural patterns such as retrieval augmentation, planning and tool use, multi-agent collaboration, and continuous evaluation loops. Each chapter provides actionable insights, real-world examples, and engineering best practices that help you move from experimentation to reliable deployment. Key topics include: i. Foundation model capabilities and system roles ii. Retrieval-augmented generation and knowledge integration iii. Agents, orchestration flows, and tool interaction iv. Data engineering, embeddings, and vector indexing v. Testing, evaluation, guardrails, and responsible AI practices vi. Deployment architectures and scaling strategies vii. Monitoring, observability, and lifecycle management If you want to build AI systems that are dependable, understandable, and ready for production, this book will guide you step by step. Equip yourself with the patterns and practices that modern engineering teams rely on. Start building high-impact AI systems with confidence.

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