Model Anatomy · Episode 06 · 30-second cut · 0:33
RAG in 30 seconds: stop your AI from guessing
Find the closest chunk of your docs, put it in the prompt and answer with the source. "New hires get 15 vacation days a year," from hr-1, score 0.91.
Key moments
What you’ll needPython 3.10+
What you’ll need
- python.org/downloads →
Python 3.10 or newerRuns the code. Check yours with python3 --version. - code.visualstudio.com →
VS Code or CursorEither works; so does any editor you like. - git-scm.com/downloads →
gitGets the code from GitHub. - No packages needed: it uses only the Python standard library.
Set it up
- 1Get the code
git clone https://github.com/DayanEbrar0X/data-anatomy.ai.git cd data-anatomy.ai
- 2Make a virtual environmentIt keeps this project's packages separate from the rest of your computer.
python3 -m venv .venv source .venv/bin/activate
- 3Run it
cd ai-engineering/06-rag python3 src/rag.py
Project structure06-rag/
Project structure
ai-engineering/06-rag/Open on GitHub →
- data/
- handbook.json8 chunks of HR, IT and finance policy
- short/
- docs.pyloads the chunks, builds the vocabulary
- rag.pyrunthe code from the video
- src/
- docs.pyloads the chunks, builds the vocabulary
- rag.pymainthe code from the video
- README.mdthe idea, how to run it, a line-by-line walkthrough, things to try
ai-engineering/06-rag/short/rag.pyOpen on GitHub →
from docs import DOCS, embed, cosine
ask = "How many vacation days do new hires get?"
q = embed(ask)
scores = {d: cosine(q, embed(t))
for d, t in DOCS.items()}
best = max(scores, key=scores.get)
prompt = f"{DOCS[best]}\n\nQuestion: {ask}"
print(DOCS[best])
print("source:", best, round(scores[best], 2))Transcript
Your company's AI never read your HR handbook. It looks things up first. That's RAG. Take eight handbook chunks.
Embed the question. Real systems use a learned model. Score every chunk against it with cosine. Keep the closest and paste it into the prompt.
Print the match. Run it. New hires get 15 vacation days. Source: hr-1.
The model answers from that, and cites it. Look it up, then answer. That's RAG.
Read the lesson on GitHub →- #rag
- #llm
- #genai
- #python
