Scope note: This article explains the topic using public research and common engineering patterns. Retrieval, reranking, generation, and source-selection pipelines vary by product. Nothing here represents a disclosed universal ranking weight or a citation guarantee.
Temperature is a common generation-stage sampling parameter that changes the sharpness of the next-token probability distribution. It can affect randomness and diversity, but it is not a model ‘personality knob’ and does not independently explain source selection.
What it usually affects
All else equal and when the implementation exposes it, lower values generally concentrate probability on high-probability candidates; higher values allow lower-probability candidates more opportunity. Effects also depend on Top-P, the model, the prompt, and product constraints.
Why it does not imply a citation preference
Many products search, retrieve, and rerank before generation, and some use separate source-display components. A visible Temperature control may not govern those stages, and a product may fix or constrain sampling.
Practical implications for content teams
- Use Temperature to experiment with output stability and diversity.
- Hold controllable parameters constant during source-use tests.
- Do not derive content-writing rules from Temperature.
Boundary and conclusion
Temperature is a decoding parameter—not a webpage-quality, credibility, or citation weight.
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