Retrieval Β· RAG
Answer using real notes, not guesses
The Case. Remember how AI can confidently make things up? This is the fix real chatbots use, called RAG (Retrieval-Augmented Generation): look up real notes first, and only answer from what you actually found.
Your tools. Plain Python retrieves the best-matching note (like before), then builds the answer from it β and says ‘I don't know’ when nothing matches, instead of guessing.
Investigate. Press Run. Compare the guess (no retrieval) to the RAG answer (with retrieval) for two questions β one the notes cover, one they don't.
- Without retrieval, the AI can only guess β and guesses sound just as confident whether they're right or wrong.
- With retrieval, the AI grounds its answer in real notes, and admits when it has none. That's why RAG cuts down on hallucination.
Starting Pythonβ¦ (first time only β a few seconds)
Bundles your findings, chart and code into one PDF you can keep, print or hand in.
π§© How this code works
The tools it uses
- random β makes lucky, unpredictable choices β like rolling a dice.
What this one does
- β’ It uses chance and randomness.
How to read it, top to bottom
- 1. Import β bring in the tools (listed above).
- 2. The data β the lists and numbers near the top. These are the bits you change.
- 3. The work β a little maths, or a loop that repeats a step.
- 4. Show it β
print(...)writes words;plt.show()draws the picture.
Use it for your own problems
- Change the numbers at the top and press Run β nothing breaks, so experiment!
- Ask your own version of the question β swap in your family, your scores, your week.
- The same tools work on any numbers β that's the superpower you're learning.
Need a hint?
retrieve() only returns a note when the score is above 0 β no match means an honest 'I don't know' instead of a guess.