April 28, 20263 min read

LLM Temperature: The One Parameter That Changes Everything

LLMAI EngineeringHealthcare AIMachine Learning

Diego Muñoz Casinos

Healthcare AI Engineer

The demo below answers a question that matters in healthcare AI: at what exact point does a "stable" model start generating dangerously variable responses?

Interactive Demo

LLM Temperature Parameter

How randomness shapes next-token selection

Temperature0.70

0 — Greedy1 — Random

SoftmaxP(i) = exp(logit_i / T) / Σ exp(logit_j / T)

"The discussion was primarily ___"

40.1%

about

22.6%

would

14.7%

9.6%

6.3%

when

4.1%

makes

2.7%

T=0.70: probabilities spread across tokens — high variation across runs.

about40.1%

would22.6%

of14.7%

is9.6%

when6.3%

makes4.1%

we2.7%

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Interactive Demo

Same Prompt — Three Temperatures

Hit run to re-execute — T=0 never changes

Prompt

"The patient is a 67-year-old male. HR 89 bpm, BP 134/86 mmHg, SpO₂ 96%, temp 37.5°C. Mild fatigue for 48 h. Summarize clinical status."

T = 0.0Always identical

Hemodynamically stable. Mild hypertension noted. SpO₂ within acceptable range. Fatigue is non-specific; monitoring recommended. No immediate intervention required.

T = 0.4Controlled variation

Patient is clinically stable with borderline elevated blood pressure. Oxygenation is adequate. Persistent fatigue warrants reassessment in 4–6 hours to rule out early decompensation.

T = 1.03 different outputs

Run 1Vital signs indicate a compensated state. Mild hypertension and borderline SpO₂ noted. Fatigue may be functional. Reassessment in 24 h recommended.

Run 2Elevated BP combined with reduced SpO₂ and reported fatigue may indicate early cardiovascular or pulmonary compromise. Closer monitoring advised.

Run 3Clinical picture is concerning. Persistent hypertension, borderline oxygen saturation, and fatigue could represent early deterioration. Urgent evaluation recommended.

The three T=1 outputs change on every run — from "no intervention" to "urgent evaluation" on the same input.

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