# LLM Sampling Parameters Cheat Sheet
_temperature, top_p, penalties — what each one actually changes_
Every sampling parameter reshapes the same probability distribution. Knowing which part each one touches is the difference between tuning and guessing.
> Difficulty: intermediate  
> Version: 1.0  
> Updated: 2026-03-29  
> Categories: AI Models  
> Tags: Anthropic, Openai, Tokens

Source: https://invitationbuddy.com/cheat-sheet/llm-parameters-cheat-sheet

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## The parameters
| Parameter | Effect | Typical range |
| --- | --- | --- |
| `temperature` | Flattens or sharpens the whole distribution  0 – 1.0 (deterministic → creative) |
| `top_p` | Keeps only the smallest set of tokens summing to p  0.9 – 1.0 |
| `top_k` | Keeps only the k most likely tokens  20 – 100 |
| `presence_penalty` | Penalises a token for having appeared at all  -2.0 – 2.0 |
| `frequency_penalty` | Penalises in proportion to how often it appeared  -2.0 – 2.0 |
| `max_tokens` | Hard ceiling on the reply, not a target  Model dependent |
| `stop` | Sequences that end generation immediately  Up to 4 strings |
| `seed` | Best-effort reproducibility; never guaranteed  Any integer |

## Settings by job
| Job | Settings | Note |
| --- | --- | --- |
| `Extraction / JSON` | temperature 0  Anything above 0 will eventually break your schema |
| `Classification` | temperature 0, max_tokens tiny  Cap the reply to the label length |
| `Summarisation` | temperature 0.2–0.3  A little variety, no invention |
| `Chat assistant` | temperature 0.7  The common default for a reason |
| `Brainstorming` | temperature 0.9–1.0  Sample several and pick |
| `Code generation` | temperature 0–0.2  Correctness has one shape |

## Do not tune both
temperature and top_p both control randomness by different means. Every major provider recommends changing ONE and leaving the other at its default. Moving both makes the effect of either impossible to reason about, and is the usual cause of "it went strange and I cannot say why".

## FAQs
**Should I change temperature or top_p?**
One, never both. They control the same randomness by different means, and moving both makes the effect of either impossible to reason about. Every major provider says the same thing.

**Does temperature 0 make output deterministic?**
Close, but not guaranteed. Batching, hardware and model updates can all still change the result. Treat it as "as repeatable as this system offers", not as a promise.

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_Generated from https://invitationbuddy.com/cheat-sheet/llm-parameters-cheat-sheet_