Model Temperature

Model Temperature is a core parameter governing how AI language models generate text, directly influencing the factual reliability, creativity, and consistency of AI-generated answers. Understanding temperature is essential for anyone seeking to understand how AI search systems produce their responses.

How Temperature Works

The Token Selection Process

When an LLM generates text, it predicts one token (word or word fragment) at a time. For each position, it calculates a probability distribution over its entire vocabulary, assigning a likelihood to every possible next token.

Example probability distribution for “The capital of France is ___“:

Token	Raw Probability
Paris	0.92
Lyon	0.03
a	0.02
Marseille	0.01
the	0.01
Other tokens	0.01

Temperature modifies how these probabilities are used to select the next token.

Temperature Scale

Temperature	Behavior	Use Case
0.0	Always picks the highest probability token (greedy)	Factual answers, data extraction
0.1 - 0.3	Strongly favors high-probability tokens	Reliable, consistent responses
0.5 - 0.7	Balanced between consistency and variety	General conversation, explanations
0.8 - 1.0	More willing to explore lower-probability tokens	Creative writing, brainstorming
1.0 - 2.0	Significantly increases randomness	Experimental, highly creative outputs

The Mathematical Effect

Temperature divides the raw logits (pre-probability scores) before they are converted to probabilities through the softmax function:

• Low temperature amplifies the differences between high and low probability tokens, making the model more confident in its top choice
• High temperature flattens the probability distribution, giving lower-probability tokens a better chance of being selected

Temperature in AI Search Systems

Factual Queries

AI answer engines typically use low temperature settings when responding to factual queries. When a user asks “What year was the Eiffel Tower built?” the system needs a deterministic, accurate answer, not creative variation.

Low temperature produces:

The Eiffel Tower was built in 1889 for the World’s Fair in Paris.

High temperature might produce:

The magnificent Eiffel Tower, that iconic Parisian symbol, rose from the grounds of the 1889 Exposition Universelle…

Both are factually correct, but the low-temperature response is more direct and reliable for a search context.

Different Temperature for Different Tasks

AI platforms often adjust temperature dynamically based on the type of query:

Query Type	Typical Temperature	Reasoning
Factual questions	0.0 - 0.2	Maximum accuracy, minimum hallucination
Explanations	0.3 - 0.5	Clarity with natural language variety
Creative writing	0.7 - 1.0	Variety and originality desired
Code generation	0.0 - 0.2	Correctness is paramount
Summarization	0.2 - 0.4	Faithful to source with readable output

Temperature and Hallucination

The Hallucination Connection

Higher temperature settings increase the risk of AI hallucination. When the model is more willing to select lower-probability tokens, it is also more likely to generate plausible-sounding but incorrect information.

At low temperature:

• The model sticks closely to its most confident predictions
• Responses tend to be more factually grounded
• Output is more predictable and verifiable

At high temperature:

• The model explores less likely word choices
• Responses may include fabricated details
• Novel combinations of information may emerge, some inaccurate

Implications for AI Answer Engines

Answer engines that prioritize accuracy, such as Perplexity and Google AI Overviews, generally run at lower temperatures for factual content. This is why AI search responses tend to be more restrained and formulaic compared to creative AI applications.

Temperature and Reproducibility

Consistency Across Queries

At temperature 0, the same query should produce the same response every time (deterministic behavior). As temperature increases, the same query may produce different responses each time.

This has important implications for AEO:

• Content ranking is not static - The same content may be cited differently across identical queries at non-zero temperature
• Testing variability - Monitoring AI visibility requires running queries multiple times to account for temperature-induced variation
• Citation consistency - A source cited at temperature 0 may not always be cited at higher temperatures

How Temperature Interacts with Other Parameters

Top-P (Nucleus Sampling)

Top-P sampling is often used alongside temperature. While temperature adjusts the probability distribution, Top-P limits which tokens are eligible for selection by cutting off the least likely options.

Top-K Sampling

Top-K restricts selection to the K most probable tokens. Combined with temperature, it provides another layer of control over output quality and diversity.

Frequency and Presence Penalties

These parameters discourage repetition. Combined with moderate temperature, they produce varied but relevant outputs, which is the typical configuration for conversational AI assistants.

Why It Matters for AEO

Temperature directly affects how reliably your content appears in AI-generated answers. AI answer engines operating at low temperatures produce more consistent, factual responses, which means they are more likely to cite well-established, authoritative sources. Content that aligns with high-probability, well-known facts is more likely to be surfaced at any temperature setting.

For AEO practitioners, understanding temperature explains why AI search results can vary between queries, why some content is cited inconsistently, and why factual accuracy and clear authority signals matter so much. Content that is the clear, authoritative answer to a question is favored regardless of temperature setting, while ambiguous or weakly authoritative content is more likely to be skipped as the model becomes more deterministic.