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RAG University: Production Retrieval Systems

From Naive RAG to Hybrid Search, Cross-Encoder Reranking, Graph RAG, and Parent-Child Chunking at enterprise scale.

All TracksAdvanced AI Interview LabJAX for ML EngineeringJevAgentic AI & Multi-Agent SystemsRAG UniversityLLM Engineering & Core TransformersDistributed Big Data EngineeringDistributed & AI System Design

RAG University: Production Retrieval Systems

From Naive RAG to Hybrid Search, Cross-Encoder Reranking, Graph RAG, and Parent-Child Chunking at enterprise scale.

Chunking, Embedding & Hybrid Retrieval

Designing the physical retrieval pipeline: tokenization, dense vs sparse search, and reciprocal rank fusion.

2 Lessons
Intermediate30 min

Advanced Chunking Strategies: Semantic, Hierarchical & Late Chunking

The greatest determinant of RAG accuracy is not the LLM or the vector database: it is chunking quality. Bad chunking breaks ideas mid-sentence, splits tables, and destroys semantic coherence.

Advanced35 min

Hybrid Search & Cross-Encoder Reranking

Vector search alone struggles with acronyms, part numbers, and exact keywords. Sparse search (BM25) struggles with synonyms and intent. Hybrid search + Reranking gives you the best of both worlds.