RAG Systems with Generative AI Designing Context-Aware and Scalable AI Systems [#1041435]

RAG Systems with Generative AI : Designing Context-Aware and Scalable AI Systems
English | 28 April 2026 | ASIN: B0GYRN4D2N | 145 pages | Epub | 5.62 MB
Artificial intelligence is rapidly transforming how systems are designed, built, and operated. Generative AI has introduced powerful capabilities-enabling machines to produce human-like responses, assist in complex workflows, and accelerate development across industries.However, there is a fundamental challenge.Generative systems can produce outputs that sound correct, but are not always grounded in real data. In enterprise environments, where accuracy and reliability are critical, this limitation becomes a significant barrier.In RAG Systems with Generative AI, Swapneswar Sundar Ray presents a practical, system-level approach to solving this problem through Retrieval-Augmented Generation (RAG)-a design paradigm that combines generative intelligence with real-time data retrieval to create context-aware and dependable AI systems.This book moves beyond theory to explore how modern AI systems are designed and deployed in real-world environments. It provides a structured framework for integrating data, retrieval, and generation into scalable, production-ready architectures.Drawing from hands-on experience in large-scale API platforms and enterprise systems, the author presents an implementation-driven perspective on applying AI to complex engineering challenges. His work includes designing high-scale API ecosystems used by external partners, where performance, consistency, and reliability are essential.A key focus of his work is integrating Generative AI and RAG into real-world systems to move from static architectures toward adaptive, context-aware solutions. He has developed intelligent API frameworks that incorporate automated validation, schema correction, and real-time API generation from specifications-reducing manual effort while improving standardization and accuracy.He has also designed automated API sandbox environments that simulate production-like systems with minimal input. By combining AI-driven validation, dynamic data generation, and retrieval-based context alignment, these systems enable developers and partners to interact with realistic APIs within minutes instead of days, significantly improving onboarding efficiency and reducing engineering overhead.This work reflects a broader shift from manual workflows to intelligent, system-driven automation, where AI is used not only to generate outputs but to orchestrate processes, enforce standards, and improve system reliability at scale.What You Will LearnHow generative AI systems work-and where they fall shortHow RAG improves accuracy through contextual groundingHow to design data pipelines and retrieval systemsHow embeddings enable semantic understandingHow prompt design influences output qualityHow to scale AI systems for production environmentsHow to implement governance, security, and observabilityWho This Book Is ForSoftware engineers and developersSystem designers and technical professionalsAI practitioners working on real-world applicationsAnyone building reliable, scalable AI systemsWhy This Book MattersAs organizations adopt AI-driven solutions, the need for systems that are not only intelligent but also trustworthy becomes critical. This book provides a clear path for bridging the gap between generative capability and real-world reliability through practical system design.This is not just a book about AI.It is a guide to designing systems where intelligence is grounded, scalable, and aligned with real-world requirements.