ML models are probabilistic. Imagine that you want to know what’s the best cuisine in the world. If you ask someone this question twice, a minute apart, their answers both times should be the same. If you ask a model the same question twice, its answer can change.

This probabilistic nature makes AI great for creative tasks.

However, this probabilistic nature also causes inconsistency and hallucinations. It’s fatal for tasks that depend on factuality. Recently, I went over 3 months’ worth of customer support requests of an AI startup I advise and found that ⅕ of the questions are because users don’t understand or don’t know how to work with this probabilistic nature.

To understand why AI’s responses are probabilistic, we need to understand how models generate responses, a process known as sampling (or decoding). This post consists of 3 parts.

1. Sampling: sampling strategies and sampling variables including temperature, top-k, and top-p.
2. Test time sampling: sampling multiple outputs to help improve a model’s performance.
3. Structured outputs: how to get models to generate outputs in a certain format.

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