BLIZARRA AI EDITIONS
Mastering Retrieval-Augmented Generation
Vector databases, hybrid search, cross-encoder rerankers, and production chunking strategies.
PDF e-Book228 pages
Publication Specifications
FORMATDRM-Free PDF
LENGTH228 Pages
TIERcore Edition
PUBLISHERBlizarra Ltd
Core228 pages • Instant Download
Mastering Retrieval-Augmented Generation
Vector databases, hybrid search, cross-encoder rerankers, and production chunking strategies.
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About this Book
Naive RAG fails in production. This comprehensive manual details the complete pipeline required to build high-accuracy question-answering systems over millions of internal documents: semantic chunking, reciprocal rank fusion (RRF), parent-document retrievers, contextual compression, and automated evaluation with Ragas.
Target Audience: Software engineers and ML practitioners building enterprise search and knowledge bases.
What You Will Learn & Build
Master chunking heuristics: semantic boundaries, markdown headers, and recursive splitting
Combine dense vector search with sparse BM25 keyword matching via Reciprocal Rank Fusion
Deploy cross-encoder reranking models to boost top-3 hit rate by over 40%
Detect and eliminate retrieval hallucinations before sending context to the generator
Table of Contents
1. Why Naive RAG Collapses Under Real WorkloadsSection 1
2. Document Parsing, OCR & Clean Extraction PipelinesSection 2
3. Semantic vs Recursive Chunking HeuristicsSection 3
4. Vector Embeddings: Dimensionality, Distance Metrics & NormalizationSection 4
5. Hybrid Search: Dense Vectors + BM25 with Reciprocal Rank FusionSection 5
6. Cross-Encoder Rerankers & Context CompressionSection 6
7. Benchmarking RAG Quality with Ragas and TruLensSection 7