Kód: 50850040
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, ... celý popis
Angličtina
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Anotácia knihy
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.
Core principles behind Retrieval-Augmented Generation
Designing end-to-end RAG architectures
Chunking, embedding, and indexing strategies
Using vector databases for efficient retrieval
Prompting LLMs with retrieved context
Evaluating relevance, accuracy, and latency
Hardening RAG systems for production environments
The focus is on system design and robustness, not surface-level examples.
This guide is ideal for:
Software engineers building AI-powered applications
AI and ML engineers working with LLMs
Data engineers supporting knowledge systems
Architects designing enterprise AI platforms
Teams deploying RAG in production
Basic programming experience and familiarity with LLM concepts are recommended.
Pure LLMs generate text.
RAG systems retrieve knowledge and reason over it.
Well-designed RAG systems:
Reduce hallucinations
Improve factual accuracy
Enable enterprise data integration
Support auditability and updates
This book teaches how to engineer RAG systems you can trust.
Build RAG Systems That Hold Up in Production
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18.32 €
AngličtinaOsobný odber Bratislava a 12840 dalších
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