Book Description
System design is now a critical skill for AI professionals, enabling them to integrate data pipelines, model serving, orchestration, and monitoring into cohesive production ecosystems. Mastering AI System Design will guide you through that complete journey—from understanding design principles and data workflows to building deployable AI architectures. It introduces core components of AI system design such as data engineering, model selection, evaluation metrics, API integration, and lifecycle management.

Each chapter blends theory, architecture diagrams, and code-driven blueprints that cover real-world use cases—LLMs and prompt engineering, Retrieval-Augmented Generation (RAG), fine-tuning, supervised and unsupervised learning systems, recommendation engines, edge AI deployment, and multimodal transformers.

By the end, you will be well-equipped to analyze trade-offs, design scalable inference pipelines, ensure model reliability, and apply system design frameworks for interviews and enterprise AI applications with confidence.

Table of Contents
1. Introduction to AI System Design
2. Crafting Intelligent Systems Using Prompt Engineering
3. Developing Retrieval-Augmented Generation Systems
4. Enhancing Systems Through LLM Finetuning
5. Designing Financial Risk Prediction Systems Using Supervised Learning
6. Implementing Unsupervised Learning Systems
7. Building Recommendation Systems for E-Commerce
8. Building Image Classification Models for Edge Devices
9. Designing Sequence-to-Sequence Systems
10. Building Domain-Specific LLMs from Scratch
11. Building Multimodal Applications for Healthcare
Index