20 March 2025 – Saima Mushtaq brings more than twenty years of experience at the intersection of technology, entrepreneurship, research, and artificial intelligence. Beginning her journey in software engineering, she progressed through roles as a researcher, machine learning and NLP specialist, technology executive, founder, and CTO. She has built and led engineering teams, launched technology companies, and worked across AI applications ranging from recommendation systems and logistics to sustainability and intelligent platforms. Her career has consistently focused on applying emerging technologies to practical problems rather than simply observing technological change. Today, as a technology leader and author, Mushtaq is focused on helping practitioners understand what it takes to build dependable AI systems beyond the experimental stage. Her book, Production AI, draws on this practical experience to provide a grounded perspective on the architecture, deployment, reliability, and future of modern AI.
As organizations rapidly adopt generative artificial intelligence, the challenge is no longer simply demonstrating what AI can do. The greater challenge is building systems that can operate reliably, securely, and economically at scale. Production AI: A Practitioner’s Guide to LLMs, RAG, Agents & Real World Deployment addresses this challenge with a practical framework for engineering AI systems beyond the prototype stage.
Written by technology leader and AI practitioner Saima Mushtaq, the book explores the fundamental shift from traditional software to intelligence systems and examines what this transformation means for engineers and technology organizations. It introduces the concept of the “production gap,” the distance between impressive AI demonstrations and systems capable of handling real users, unpredictable inputs, operational constraints, and continuous quality requirements.
The guide takes readers through the core architecture of modern AI systems, including large language models, prompt engineering, retrieval augmented generation, AI agents, orchestration, model serving, observability, evaluation, security, governance, and AI economics.
A central principle of the book is that AI should be approached as infrastructure rather than simply another product feature. This means establishing quality standards, monitoring performance continuously, preparing incident response procedures, and designing systems to manage failure from the beginning.The book also features case studies from major AI deployments, including GitHub Copilot, Morgan Stanley, Klarna, Harvey Legal, and Notion AI, connecting architectural principles with real world applications.
Production AI is intended for engineers, technical leaders, founders, and organizations seeking to move from experimenting with AI to building dependable systems that can withstand the demands of production.
