RAG & Semantic Search
Connecting AI to real-world knowledge — retrieval-augmented generation, vector databases, embeddings, and semantic search patterns.
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What Is Sentence Transformers and How Contrastive Learning Produces Sentence-Level Embeddings
Sentence Transformers turns transformers into sentence encoders via contrastive learning. Covers bi-encoders, loss …
From Cosine Similarity to Anisotropy: Prerequisites and Hard Limits of Sentence-Level Embeddings
Sentence Transformers encode meaning as geometry. Learn the prerequisites, token limits, and anisotropy traps that …
What Is Multi-Vector Retrieval and How Late Interaction Replaces Single-Embedding Search
Multi-vector retrieval stores per-token embeddings instead of one vector per document. Learn how ColBERT MaxSim scoring …
From Embeddings to Token-Level Matching: Prerequisites and Hard Limits of Multi-Vector Search
Multi-vector retrieval trades storage and latency for token-level precision. Learn the prerequisites, storage math, and …
What Is Vector Indexing and How HNSW, IVF, and Product Quantization Make Nearest-Neighbor Search Fast
Vector indexing replaces brute-force search with graph, partition, and compression strategies. Learn how HNSW, IVF, and …
Memory Blowup, Recall Collapse, and the Hard Engineering Limits of Vector Indexing at Scale
HNSW memory grows linearly with connectivity while PQ recall collapses on high-dimensional embeddings. Learn where …
From Distance Metrics to Graph Traversal: Prerequisites for Understanding Vector Index Internals
Distance metrics, high-dimensional geometry, exact vs approximate search — the prerequisites you need before HNSW and …
Vector Search for Developers: What Transfers and What Breaks
Vector search mapped for backend developers. Learn which database instincts transfer, where approximate results break …
What Are Similarity Search Algorithms and How Nearest Neighbor Methods Find Matching Vectors
Similarity search algorithms find matching vectors by measuring geometric distance, not keywords. Learn how HNSW, PQ, …
From Distance Metrics to Index Structures: The Building Blocks of Vector Similarity Search
Similarity search combines distance metrics, index structures, and quantization. Learn how HNSW, IVF, LSH, and product …
Curse of Dimensionality, Recall vs. Speed, and the Hard Limits of Approximate Nearest Neighbor Search
High-dimensional similarity search faces hard mathematical limits. Explore the curse of dimensionality, recall-speed …