Term tag: RAG

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.

AI Architecture

How a production AI system is assembled: retrieval, context assembly, the model call, tool execution, validation, logging and evaluation.

AI Hallucinations

Models hallucinate because training and benchmarks reward guessing over admitting ignorance: a guess sometimes scores, "I don't know" scores zero every time.

Chunking

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

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.

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.

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.

Vector Database

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