Name: Building Production RAG Systems. Vector Databases, Embeddings, and Retrieval Engineering (en Inglés)
SKU: 67366089
Price: 64.82 PEN
Availability: InStock
Author: ChatVariety Team
ISBN: 9798197444844

portada Building Production RAG Systems. Vector Databases, Embeddings, and Retrieval Engineering (en Inglés)

Building Production RAG Systems. Vector Databases, Embeddings, and Retrieval Engineering (en Inglés)

Formato

Libro Físico

Editorial

Independently published

Autor

ChatVariety Team

Año

2026

Idioma

Inglés

N° páginas

Encuadernación

Tapa Blanda

Dimensiones

22.9x15.2x0.4 cm

ISBN13

9798197444844

Building Production RAG Systems. Vector Databases, Embeddings, and Retrieval Engineering (en Inglés)

Chatvariety Team (Autor) · Independently published · Tapa Blanda

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Reseña del libro "Building Production RAG Systems. Vector Databases, Embeddings, and Retrieval Engineering (en Inglés)"

Build Enterprise RAG Systems That Actually Work in Production

Retrieval-Augmented Generation (RAG) is the definitive architecture for enterprise AI, yet transitioning from a simple prototype to a reliable production deployment remains a massive engineering challenge. Building Production RAG Systems is the definitive guide for software engineers and AI developers looking to bridge the gap between a proof-of-concept and a system trusted by thousands of daily users.

Rather than rehashing basic prompt engineering, this comprehensive technical resource dives deep into the complex architecture, system design, and retrieval engineering required to scale AI applications. You will learn to eliminate hallucinations, dramatically improve answer quality, minimize query latency, and prevent runaway API costs.

Inside this book, you will master:

Advanced Chunking Strategies: Implement fixed-size, semantic, recursive, and late-chunking approaches for optimal context preservation.Vector Database Selection: Choose the right engine for your workload, including deep dives into Pinecone v3, Weaviate, ChromaDB, and pgvector.Hybrid Search & Reranking: Combine dense retrieval with BM25 sparse search, and build reranking pipelines using cross-encoders and ColBERT v2.Robust Evaluation Pipelines: Move beyond intuition and evaluate RAG quality using RAGAS, TruLens, and custom metrics.Cost Optimization & Reliability: Tune approximate nearest-neighbor search, implement circuit breakers, caching, and fallback strategies.

Written specifically for engineers who understand AI fundamentals and need production-grade architectural patterns. Stop struggling with brittle generative AI deployments and start building highly reliable, cost-effective, and scalable enterprise retrieval systems today.