Technical

RAG (Retrieval-Augmented Generation)

An architecture that combines a retrieval system with an LLM — the model first retrieves relevant documents, then generates answers grounded in that retrieved context.

Explained at five levels

Level 1

When the AI looks up real information in a library before answering, instead of just guessing from memory.

Level 2

A technique where AI searches through actual documents or databases first, then uses what it finds to give you a more accurate answer.

Level 3

An architecture that combines a retrieval system with an LLM — the model first retrieves relevant documents, then generates answers grounded in that retrieved context.

Level 4

A hybrid approach pairing a retrieval module (dense or sparse) with a generative model, allowing the system to condition its output on dynamically retrieved evidence, reducing hallucination and enabling knowledge updates without retraining.

Level 5

A retrieve-then-generate pipeline where a bi-encoder or cross-encoder retriever supplies grounding documents to a conditional language model, decomposing world knowledge into a parametric component (model weights) and a non-parametric component (external index).

Definitions are educational summaries. Terminology can vary by source and context.

Sources