One word, then one sentence
The same model, now asked a question instead of handed a sentence to finish. First it gets one word to answer in, then a sentence. It is still predicting what comes next. Guide: Why fluency tells you nothing →
One word
We ran it 20 times.
What the model thought could come next
The probability it gave each word, before choosing.
What it actually said, 20 times
Each box is one run. Same sentence, same model, every time.
Asked as a question, the probability spreads out. The favorite takes about a quarter, and 20 runs give 11 different words. The word the model said most often wasn’t even its favorite.
One sentence
Two runs, same prompt, same settings. Both began “Prospect theory addresses decision-making under risk but leaves a significant gap in…” and then had to pick the next word.
What the model thought could come next
“Prospect theory addresses decision-making under risk but leaves a significant gap in ___”
What to notice
- It’s still predicting what comes next, not answering your question. The question is just more text to continue from.
- Two runs, two different research gaps. Emotion and long-term consequences in one, ambiguity in the other. Neither was chosen because it was the better answer.
- Both read like a confident claim. Each word is predicted from everything before it, so the sentence always hangs together, whichever way it went.
Limits
One prompt of each kind, one model. Your own runs will come out differently, draw by draw.