Read the logprobs, not the answer

Every token a model emits comes with a probability distribution over the whole vocabulary. The decoded text only shows you the winner. The logprobs show you the race.

When you pull them back, scan for the spots where the top token barely beat the runner-up. Those are the seams. Names, dates, citations, and the first token of a refusal are where you most often find a coin-flip dressed up as a confident sentence.

A practical loop: request the top 5 logprobs per token, render them as a heatmap over the output, and read the cold spots first. That is usually where the hallucination lives.