What this lesson covers.
This module is one of 42 in the curriculum. Below is the canonical interactive lesson — tabs, cards, and diagrams from the source repo, rendered inside the course shell. An audio narration runs alongside it - the sticky player at the top of the page plays the full Module 20 clip.
If you prefer to read first and play with the demos after, the interactive lesson sits below this section. If you'd rather hear it narrated while you scroll, hit play on the sticky audio bar at the top — or just let it autoplay.
The lesson itself.
Retrieval-Augmented Generation (RAG)
Large Language Models are frozen in time the day they finish training. If you ask them about proprietary data or recent events, they confidently hallucinate. RAG solves this by turning the system into an "open-book test". But first, data must be systematically parsed, chunked, transformed into mathematical vectors, and pushed into a dense Vector Database.
Security
Manual.pdf
"The top secret override passcode for the mainframe is: OMEGA-9."
Try it: rag pipeline.
Type a question. Watch the four stages run - embed, retrieve, augment, generate - against an 8-document mini vector store.
Further reading.
The canonical references for this module. External links open in a new tab.
What to read next.
Use the pager below to move sequentially through the curriculum, or jump to any module from the course index. Each track has a "Prereq: ↑ foundation" callout so you can backfill anything that wasn't clear.