AI Foundationspredict · compress · act

concepts → Algorithm

Algorithm

Retrieval-augmented generation

also called RAG

decade5 connections

look facts up and put them in the context, instead of storing them in the weights

Where it sits

The prism has six jobs across and eight layers down. Its primary cell isinfer × L6. Hatched cells cannot exist — a GPU does not learn, an institution does not infer.

What must come first — and what it unlocks

Left to right is reading order, derived from the prerequisite_of edges. Nothing here is hand-ordered: the diagram is the graph.

Vector databaseRetrieval-augmented generationnothing depends on it yet — a leaf in the reading order

Before it: Vector database

What kind of thing it is — and what it is made of

It is made of: Vector database, Chunking, Semantic search.

Where to read it

The chapter that introduces it, and any chapter that uses it again.

35Memory Outside the Weightsact 8 · The Agent Infrastructure

Where it comes from

Every connection

All 5 edges touching this node, grouped by relation family — the sections above are highlights from this list. Colours match the relation families inthe atlas.

Structure · 3
has as a partVector databaseSystem
has as a partChunkingMechanism
has as a partSemantic searchMechanism
Order · 1
requiresVector databaseSystem
Flow · 1
usesWord embeddingMechanism

This page is a projection of one node in src/data/concepts.ts. It has no prose file of its own — 230 declared edges produce all 236 of these pages. Edit an edge and both endpoints change.