Term tag: embedding

Chunking

Chunking splits documents into smaller passages before embedding them for retrieval. Chunk size, overlap, and splitting strategy quietly decide RAG quality.

Embedding

What an embedding vector actually is, why cosine similarity beats Euclidean distance, what dimensions buy you, and where the geometry misleads you.

GraphRAG

GraphRAG runs RAG over a knowledge graph and community summaries built from a corpus, answering global questions that plain vector RAG cannot.

Hybrid Search (RRF)

Hybrid search runs keyword (BM25) and vector retrieval together, then fuses their ranked lists with Reciprocal Rank Fusion (RRF), a score-agnostic method.

Positional Encoding (RoPE)

Positional encoding tells a transformer where each token sits in a sequence. Why self-attention needs it, and how rotary embeddings (RoPE) work.

Recommendation Systems

A recommendation system predicts what a user will want — a film, product, or song — from patterns in past behavior, and surfaces it automatically.

Representation Learning

Representation learning is machine learning that discovers the useful features of raw data automatically, instead of humans hand-engineering them.

Reranking (Cross-Encoder)

Reranking rescores the top-N results from a fast retriever with a slow, accurate cross-encoder that reads the query and document together.

Retrieval-Augmented Generation (RAG)

A technique that retrieves relevant documents at query time and feeds them into a language model's prompt, so it answers from current, external facts.

Semantic Search

Semantic search finds results by meaning, not exact keywords: it embeds the query and documents as vectors and returns the nearest ones.

Semantic Understanding

Whether an AI grasps meaning well enough to act correctly on novel inputs, or is pattern-matching that breaks on rephrasing — and why that line is contested.

Vector Database

A vector database stores embeddings with metadata, builds an approximate-nearest-neighbour index over them, and serves filtered similarity queries at scale.

Vector Search

Vector search returns the stored vectors nearest to a query vector by a distance metric — the retrieval primitive under semantic search, RAG and recommenders.

Vision Transformer (ViT)

A Vision Transformer (ViT) cuts an image into fixed patches, embeds each as a token, and runs a transformer encoder — trading CNN inductive bias for scale.