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Production RAG and GenAI Systems
From Retrieval Fundamentals to Production-Ready AI Architectures
Learn how to design and build production-ready RAG and GenAI systems—from retrieval fundamentals and evaluation to agents, observability, security, and end-to-end architecture.
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PDF
76
Pages
About
About the Book
Production RAG and GenAI Systems is a practical guide to designing, building, evaluating, and operating Retrieval-Augmented Generation (RAG) and modern GenAI systems in production.
Starting with the foundations of embeddings, vector search, retrieval, context construction, and generation, the book gradually moves toward the engineering challenges that matter in real-world systems: document ingestion, chunking, metadata filtering, hybrid retrieval, reranking, evaluation, observability, security, reliability, cost, and production architecture.
The book also explores how RAG fits into the broader GenAI landscape, including tool use, agents, workflows, and architectural patterns for building systems that go beyond simple question answering.
Rather than focusing on a particular vendor or framework, the book emphasizes the underlying engineering principles and mental models that can be applied across technologies and platforms.
It is written for software engineers, backend engineers, architects, and AI engineers who want to move beyond experimenting with RAG and understand how to design GenAI systems that are reliable, measurable, maintainable, and ready for production.
Author
About the Author
Arvind Kumar
Arvind Kumar is a Senior Software Engineer and Software Architect with 15+ years of experience building enterprise applications, distributed systems, cloud platforms, and AI-enabled solutions.
His experience spans software architecture, backend engineering, cloud-native systems, microservices, data platforms, and modern Generative AI application development. He is particularly interested in the engineering principles and architectural patterns required to build reliable, scalable, and production-ready AI systems.
He writes about software engineering, cloud architecture, RAG, Generative AI, and the practical challenges of taking modern AI systems from experimentation to production.
Also by the Author
Also by the Author
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