Vector Search

Approximate Nearest Neighbor (ANN) search is the engine behind Retrieval-Augmented Generation (RAG) and semantic search. Performing exact k-Nearest Neighbors (k…

First-generation Retrieval-Augmented Generation (RAG) systems relied exclusively on naive Vector Search (semantic similarity lookups over dense embeddings). Whi…

Artificial intelligence terminology moves so fast that concept definitions quickly become bloated with marketing fluff and overlapping jargon. Understanding the…

RAG taxonomy gets confusing because people mix three different ideas: architecture levels, retrieval tricks, and production maturity. Naive RAG, Simple RAG, Gra…

Vector Search Filters matters when a team has to turn an AI idea into a system other people can trust. The useful question is not whether the term sounds advanc…

A search team upgrades their embedding model for better quality, re-embeds only newly added documents with it, and leaves millions of older documents on the pre…

A support document explaining refund policy, shipping policy, and warranty terms gets embedded as one 2,000-token chunk — and a query about refunds retrieves it…

Search architecture in modern applications has expanded beyond traditional exact keyword matching to encompass semantic intent understanding powered by vector e…

Large Language Model (LLM) API calls — such as requesting completions from Google Gemini 1.5 Flash — introduce significant financial costs ($/token) and latency…

Generative AI, Retrieval-Augmented Generation (RAG), and semantic image search rely on high-dimensional vector embeddings (1536-dimensional float32 arrays from …

Ask ChatGPT a general-knowledge question and it answers from what it learned during training. Ask a support bot "what's your refund window for a damaged item bo…