Ask your AI Ask your AI to turn this code into a guided bug-finding exercise.
Claude ChatGPT I am reading "Why does scaled dot-product attention divide query-key scores by the square root of the key dimension?" on CodeWiki (https://codewiki.com/practice/mcq/ai/checkpoint-1/checkpoint-1-attention-scaling/).
Context (verbatim from the page):
"""
Why does scaled dot-product attention divide query-key scores by the square root of the key dimension?
To keep large dot products from saturating softmax and producing very small gradients
To make every token receive exactly the same attention weight
To remove the need for learned query, key, and value projections
"""
Introduce three subtle bugs into the example above and let me find them. Reveal each answer only after I have guessed.
Answer in English. Keep code examples in AI and LLM engineering. Where you are unsure, say so.