Pick a depth. Each prompt opens in your AI pre-loaded with the lesson. Click a row to preview the prompt.
The difference between a portfolio project and a tutorial is not the tech stack, it is whether anything was measured. Accuracy delta against a baseline, availability through an induced outage, agreement with a human labeler, break-even request volume — one number, honestly obtained, does more work in an interview than three more repositories. The reason is that a number forces every earlier decision to be real: you cannot report accuracy by hop count without building a stratified question set, and you cannot report break-even without counting idle GPU time. Pick the number first and the rest of the project stops being optional.
Write the claim you intend to make before you start, as a sentence with a blank in it. If you cannot fill the blank with a measurement procedure, the project is not scoped yet. Keep this file in the repo root and update it as the real numbers land — the gap between the predicted and actual number is itself interesting.
# claim.md — written on day 1, before any code
## The claim
"Graph-augmented retrieval answers 3-hop questions at ___% accuracy
versus ___% for vector-only retrieval, on the same 100-question set."
## How it gets measured
- question set: 100 items, stratified 20/30/30/20 across
single-hop / two-hop / three-hop / out-of-scope
- both systems answer the same set, same model, same temperature
- graded by exact-match on the expected entity set, not by vibes
- reported broken out BY HOP COUNT, because the aggregate hides the story
## What would falsify it
- parity at three hops -> the graph is not earning its cost
- the gap only appears on questions I wrote after seeing the output -> leakage
## Cost of being honest
Graph ingestion: one-time $___ . Query latency: +___ ms vs vector.
Both go in the README next to the accuracy number.python3 main.py