AXLE · RAG & Context Engineering
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Weekend Challenge · Week 1

Catch Your Model Lying

Before trusting retrieval to fix hallucination, you need to see hallucination — systematically, in your own model, with your own eyes.

Your mission

  1. Write hallucination_probe.py: ask your local model 20 questions — 5 each across four categories: stable general knowledge, niche long-tail facts, post-training-cutoff events, and your capstone domain
  2. Score every answer yourself: ✅ correct, ❌ wrong, 🤷 refused/hedged
  3. Write a 5-sentence conclusion: in which categories does retrieval earn its keep?
Deliverable

A results table (markdown or CSV) + conclusion, committed with message Week 1 challenge: hallucination probe.

Stretch goal

Re-run all 20 questions at temperature 0 and 1.0. Does any category change verdict? What does that imply about 'just lower the temperature' as a hallucination fix?