Term tag: information retrieval

Agent Memory

Agent memory is how an AI agent stores, retrieves, and summarizes information so it works across long tasks and sessions without overflowing its context window.

Chunking

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

Generative Engine Optimization (GEO)

GEO — also called AEO — is optimizing content so AI answer engines cite it. From a 2024 KDD paper that found keyword stuffing backfires.

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.

Information Retrieval (IR)

Information retrieval ranks documents against a query. How inverted indexes, TF-IDF and BM25 score, and how precision, recall, MAP and nDCG measure it.

Knowledge Graphs (KG)

A knowledge graph stores facts as typed links between named things so one query can join across several. The structure is easy; the curation is what costs.

llms.txt

A proposed Markdown file at /llms.txt that gives AI systems a clean map of a site. Google ignores it; coding agents and doc tools use it heavily.

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.

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.

Text Analysis

Turning unstructured text into structured data — the concrete NLP task catalog: classification, sentiment analysis, entity extraction and topic modeling.

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.