RAG and Retrieval: Vector Search, Indexing, and Ranking

RAG & Retrieval

Indexing, chunking, ranking, and retrieval strategies for production search.

RAG and Retrieval covers the search layer behind grounded AI systems, including index structure, approximate nearest neighbors, ranking, and retrieval quality.

May 23, 2026•RAG & Retrieval

When RAG Finds the Right Document but Gives the Wrong Answer: Chunk Size, Overlap, and Retrieval Quality

Why RAG retrieves the right document and still answers incorrectly: chunk size, overlap, semantic boundaries, parent-child retrieval, and metrics.

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Feb 28, 2026•RAG & Retrieval

Why Is Vector Search So Fast? HNSW and IVF-PQ Explained With the Math

A walkthrough of approximate nearest neighbor search covering HNSW graphs, inverted file indexes, product quantization, and IVF-PQ with worked examples and memory analysis.

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