{"product_id":"enterprise-llm-engineering-designing-production-grade-rag-systems-with-databricks-vector-search-and-generative-ai-architecture-9798250578684","title":"Enterprise LLM Engineering: Designing Production-Grade RAG Systems with Databricks, Vector Search, and Generative AI Architecture","description":"\u003cp\u003e • Author(s): Kaelen Draycott\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Natural Language Processing\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eAre you building LLM applications that work beautifully in demos-but fall apart in production?\u003cbr\u003eHave you experimented with Retrieval-Augmented Generation, only to struggle with latency, hallucinations, scaling, or governance?\u003cbr\u003eDo you wonder what it really takes to design enterprise-grade systems that executives can trust and engineers can maintain?\u003c\/p\u003e\u003cp\u003e\u003cb\u003eEnterprise LLM Engineering: Designing Production-Grade RAG Systems with Databricks, Vector Search, and Generative AI Architecture\u003c\/b\u003e by Kaelen Draycott is not another surface-level AI book. It's a deep, practical conversation about what happens after the prototype-when real users, real data, and real infrastructure enter the picture.\u003c\/p\u003e\u003cp\u003eWhat does \"production-grade\" actually mean in the world of LLMs?\u003cbr\u003eIs it just plugging a model into a vector database?\u003cbr\u003eOr is it about architecture, observability, governance, cost control, security, evaluation pipelines, and long-term maintainability?\u003c\/p\u003e\u003cp\u003eIf you've asked those questions, this book was written for you.\u003c\/p\u003e\u003cp\u003eYou'll explore how Retrieval-Augmented Generation truly works under the hood-and more importantly, how to engineer it responsibly. How do you structure ingestion pipelines? How do you design effective chunking strategies? How do you optimize embeddings and vector search for both precision and recall? And how do you reduce hallucinations without sacrificing creativity?\u003c\/p\u003e\u003cp\u003e\u003cb\u003eBecause building RAG is easy. Building reliable RAG is engineering.\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eThis book walks you through the realities of enterprise LLM systems: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e\u003cp\u003eDesigning scalable data workflows in Databricks\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eIntegrating vector search architectures effectively\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eStructuring prompts and context for deterministic outcomes\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eImplementing evaluation frameworks that actually measure quality\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eManaging costs and performance at scale\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eMonitoring, logging, and continuous improvement pipelines\u003c\/p\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eHave you considered how governance impacts your LLM deployments?\u003cbr\u003eHow do you prevent data leakage?\u003cbr\u003eWhat happens when your vector store grows to millions-or billions-of embeddings?\u003cbr\u003eHow do you future-proof your architecture against rapidly evolving model ecosystems?\u003c\/p\u003e\u003cp\u003e\u003cb\u003eEnterprise AI is not just about intelligence-it's about responsibility, stability, and design clarity.\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eKaelen Draycott approaches LLM systems not as magic boxes, but as engineering challenges. You'll think architecturally. You'll think systematically. You'll start asking better questions about reliability, retrieval optimization, latency trade-offs, hybrid search strategies, and system context layering.\u003c\/p\u003e\u003cp\u003eAnd perhaps most importantly: \u003cbr\u003eAre you designing systems that merely respond... or systems that reason with controlled context?\u003c\/p\u003e\u003cp\u003eWhether you're a data engineer, ML engineer, AI architect, or technical leader, this book challenges you to move beyond experimentation and into mastery. It speaks to professionals who understand that real AI impact happens when infrastructure, data, and models align with intention.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eThis is where generative AI stops being hype-and starts becoming architecture.\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eAre you ready to stop stitching tools together and start designing cohesive systems?\u003cbr\u003eAre you ready to move from prompt tinkering to production engineering?\u003cbr\u003eAre you ready to build LLM systems that enterprises can actually deploy?\u003c\/p\u003e\u003cp\u003eThen this book is your blueprint.\u003c\/p\u003e\u003cp\u003eStep into the future of enterprise AI.\u003cbr\u003eBuild smarter. Build stronger. Build production-grade.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eGet your copy today and start engineering LLM systems that are built to last.\u003c\/b\u003e\u003c\/p\u003e","brand":"Independently Published","offers":[{"title":"Paperback","offer_id":47568449601687,"sku":"9798250578684","price":2581.0,"currency_code":"INR","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9798250578684.webp?v=1774868390","url":"https:\/\/atlanticbooks.com\/products\/enterprise-llm-engineering-designing-production-grade-rag-systems-with-databricks-vector-search-and-generative-ai-architecture-9798250578684","provider":"Atlantic Books","version":"1.0","type":"link"}