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Building Production RAG Systems: Vector Databases, Embeddings, and Retrieval Engineering
Paperback
Series: Production AI Engineering
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Publisher: Independently Published
Pages: 88
Weight: 0.28
Height: 0.18 Width: 6.00 Depth: 9.00
Language: English
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.
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