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Artículo: AMZ-B0GJ6BBXZJ
RAG from Scratch: Building Robust Retrieval-Augmented Generation Systems
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Kindle
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0.84 kg
0.84 kg
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Amazon
Amazon
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USA
USA
Sobre este producto
- Great AI answers start with the right data, not bigger models.Large language models are powerful—but unreliable when they operate without context. Retrieval-Augmented Generation (RAG) solves this by grounding AI responses in real, trusted data. This book shows how to build robust RAG systems from the ground up, focusing on reliability, accuracy, and production readiness.RAG from Scratch is a practical guide for developers and architects who want to move beyond toy demos and design RAG pipelines that work in real-world systems. What You’ll Learn in This BookCore principles behind Retrieval-Augmented GenerationDesigning end-to-end RAG architecturesChunking, embedding, and indexing strategiesUsing vector databases for efficient retrievalPrompting LLMs with retrieved contextEvaluating relevance, accuracy, and latencyHardening RAG systems for production environmentsThe focus is on system design and robustness, not surface-level examples. Who This Book Is ForThis guide is ideal for:Software engineers building AI-powered applicationsAI and ML engineers working with LLMsData engineers supporting knowledge systemsArchitects designing enterprise AI platformsTeams deploying RAG in productionBasic programming experience and familiarity with LLM concepts are recommended. Why RAG Is Essential for Real AI SystemsPure LLMs generate text. RAG systems retrieve knowledge and reason over it.Well-designed RAG systems:Reduce hallucinationsImprove factual accuracyEnable enterprise data integrationSupport auditability and updatesThis book teaches how to engineer RAG systems you can trust. Build RAG Systems That Hold Up in Production
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