I test sampling settings with a five-request budget

When an AI answer feels too predictable or wanders off course, changing a sampling setting can help. But turning several knobs at once makes it hard to tell what worked. I use a five-request budget for a first comparison: enough to notice differences without paying for an afternoon of aimless experiments.

Start with one task and a clear acceptance test

Choose a small task you actually need done, such as summarizing lecture notes, explaining a function, or suggesting names for a side project. Keep the prompt, model, and output limit unchanged throughout the test.

Before making a request, decide what a useful answer looks like. For a summary, that might mean preserving three key facts without adding claims. For names, it might mean producing several distinct suggestions that meet your constraints. Sampling settings affect how an answer is selected, not whether your instructions are clear.

Picture the model choosing its next token

A model generates text by repeatedly choosing a token, which can be a word, part of a word, or punctuation. At each choice, it assigns probabilities to possible next tokens. Temperature and top_p influence how the next token is sampled from those possibilities.

Imagine the next word could be “clear,” “helpful,” or “unexpected,” with different probabilities. These settings do not add knowledge or provide a direct accuracy control. They change the selection process. That distinction matters when an answer is wrong: adjusting randomness is not a substitute for checking sources or improving the prompt.

Use temperature to adjust how strongly favorites win

Lower temperature concentrates probability on tokens the model already considers more likely. Higher temperature spreads probability more broadly, giving less likely choices a better chance.

For an initial test, a low value such as 0.2 can suit a tightly constrained explanation or summary, if the model supports it. A higher value such as 0.8 may produce more varied phrasing or ideas. These are starting points, not universal recommendations; supported ranges and behavior differ across models.

Temperature zero does not guarantee identical results, and low temperature does not guarantee truth. A model can confidently select an incorrect answer. Higher temperature also does not automatically make an answer more creative or useful.

Use top_p to limit the candidate pool

Top_p, also called nucleus sampling, keeps the smallest group of highest-probability tokens whose combined probability reaches a chosen threshold. Sampling then happens within that group.

Suppose three tokens have probabilities of 60%, 25%, and 15%. At top_p 0.8, the first two together reach 85%, so they form the candidate pool. At top_p 1, this filter does not exclude candidates based on cumulative probability.

Unlike temperature, top_p changes which tokens remain eligible rather than simply reshaping their relative chances. The controls can interact, and implementation details vary. For a clean first experiment, change only one and leave the other at its documented default.

Spend five requests on a controlled comparison

Use your first request to establish a baseline with the model’s documented defaults. Then make two requests at a lower temperature and two at a higher temperature, keeping top_p unchanged. Repeating each setting gives you a small glimpse of variation, although five requests cannot establish a reliable statistical result.

Compare the answers against your acceptance test, not just your favorite writing style. If neither setting helps, stop and revise the prompt. If you later test top_p, run a separate small comparison with temperature fixed. Check parameter support first, because some models restrict or reject these controls.

Keep the setting that reduces total work

Temperature and top_p are not direct price controls. They can affect spending indirectly if answers become longer or require more retries. Keep an output limit in place and judge the cost of reaching one usable result.

With MyRelay, you can inspect each request’s tokens and cost while keeping your experiments within one personal budget. Create a MyRelay account to track that comparison. Keep whichever supported setting meets your needs with the least correction and repetition; there is no prize for finding the most unusual combination.