What is temperature in LLMs? Governing output variation

A compliance reviewer at an insurer pulls two transcripts from the same afternoon. Both callers asked the AI agent the same question: “What does it cost to cancel a policy mid-term?” One names the fee and notice period exactly as the policy document states them; the other adds a sentence about a waiver that belongs to a different product line. The discrepancy shows how temperature in large language models (LLMs) can affect production answers.

An engineer on the call explains that the difference comes from temperature in LLMs, a sampling setting. The reviewer asks who chose the value, who last changed it, and where they recorded that decision. Nobody in the room can answer.

How temperature changes token selection

Temperature in an LLM is a number that controls how the model picks its next token, the word or word-piece it generates. The model ranks probable token options, and teams can mistake this sampling control for a quality setting. Before each AI token, the model scores every candidate in its vocabulary, and temperature reshapes those scores before sampling.

At 1.0, temperature leaves the scores unchanged. Below 1.0, the highest-scoring candidate gains weight, and the rest lose it, so the top option wins more often. Above 1.0, the weights flatten, and the model picks lower-ranked candidates more often. Teams should therefore compare and log temperature settings instead of treating a lower value as a guarantee of reproducibility.

How low, medium, and high settings change outputs

Before comparison testing, teams must record each model API's documented temperature range and default, because vendors implement the parameter differently. The bands below describe how output tends to shift across the common 0 to above-1.0 range on a fee-cancellation question repeated ten times. Treat each band as a prediction about the degree of variation, not the quality of the answer.

• Low (0 to about 0.3): Lower settings favor top tokens, making phrasing more predictable. Ten runs of the fee question produce similar sentence shapes with minor changes to word order or connective words. Unsupported claims can still occur, so a low setting is not a substitute for grounding.

• Medium (about 0.3 to 1.0): Word choice may vary while a grounded answer stays on topic. A fee reply might open with the amount or notice period and retain the same facts. This band suits turns where natural phrasing matters and the facts are supplied by retrieval.

• High (above 1.0): Lower-ranked tokens become more likely and can introduce unexpected words or tangents. The answer may drift in ways a reviewer would flag. Reserve this band for exploratory work rather than customer-facing turns.

Task-level scoring determines whether an answer is safe and useful and which band can enter production. If a task requires exact repetition, use a fixed template rather than relying on a low temperature to approximate one. Temperature 0 increases output consistency, yet serving infrastructure can still produce variation between runs, so audit processes that expect identical replay can fail on ordinary days.

Terms often confused with temperature

Deployment reviews often attribute outcomes to temperature that belong to other controls. An International Conference on Computational Creativity (ICCC) paper on open-ended generation found temperature weakly correlated with novelty and no relationship with cohesion or typicality. The controls below shape outcomes that teams frequently misattribute to the temperature dial.

Top-p

Top-p is the control that narrows the eligible token pool. Where temperature reweights every candidate the model scored, top-p retains only the smallest high-probability token set that reaches its threshold, then samples from the renormalized set. For clearer comparison tests, alter temperature or top-p but not both, so a change in output traces to a single setting rather than the interaction between two sampling controls.

Creativity of substance

Prompt design and model capability determine whether an AI agent proposes a useful alternative to cancelling, such as a paid-up policy or a premium holiday. Temperature affects how an idea is expressed and which idea appears from the ranked candidates, not whether the underlying reasoning is sound. Evaluate substance with task-level scoring on real utterances, and treat wording variation as a separate axis from the quality of the idea itself.

Factual grounding

Correct fees depend on what the model reads, not on the sampling temperature. At 0.2, a model without policy text can still give a confident wrong fee. Teams can use Retrieval-Augmented Generation (RAG) to search a pre-processed vector database: they embed and index policy content in advance, and the retrieval system supplies relevant passages before the model generates an answer. Grounding fixes fabricated facts; temperature does not.

Retained conversation history

The context window determines whether the model retains an account number from three turns ago. Once the number falls outside that span, temperature cannot recover it, and no sampling adjustment will reconstruct information the model can no longer see. Context management, summarization, and explicit state passing keep the conversation coherent across turns, so treat history retention as an architectural decision separate from the temperature setting.

Safety

Guardrails decide what the AI agent may say about a payment it cannot verify or a claim it should escalate. Temperature affects the phrasing of permitted answers, leaving safety policy as an independent production control. On a phone call the difference is audible: a customer hears word variety as personality and a wrong fee amount, or a disallowed statement, as a broken system. Keep safety rules enforced outside the sampler.

Confabulation

The National Institute of Standards and Technology (NIST) AI 600-1 defines confabulation as a phenomenon in which generative AI systems generate and confidently present erroneous or false content. Two differently worded transcripts show wording variance; a nonexistent waiver is confabulation. Retrieval and guardrails address the fabricated content, while temperature only changes how likely the model is to select lower-ranked tokens during sampling.

Choosing and testing temperature by customer service task

A single temperature for an entire AI agent is a pilot habit. A production voice AI agent can run multiple use cases, and those intents tolerate wording variance very differently. An authentication turn that asks for a date of birth needs tightly controlled wording. Open questions about savings products can vary phrasing without costing the customer anything. One value for both can force an unnecessary compromise, so test each intent separately.

1. Set initial bands by task

The proposed task-specific bands are starting hypotheses for a comparison test.

Payment confirmations, fee amounts, and notice periods need the tightest wording, so keep them in the lowest tested band and hold its ceiling. Identity prompts and intent classification should behave like fixed steps in a flow at the same low band, because variance adds nothing to customer values.

Greetings and acknowledgements during open-ended exchanges tolerate more variety, roughly 0.5 to 0.8, which keeps the AI agent from sounding scripted. Test call summaries and translated recaps separately in each language, around 0.2 to 0.5, because a value tuned for German may not transfer to Portuguese.

2. Fix the test variables

Changing multiple variables obscures the cause of output differences and makes results impossible to attribute. Keep the prompt template and the model version fixed for the duration of the test, and change only the temperature value between runs. Record the exact model identifier, including any minor version suffix, alongside the prompt hash so a later reviewer can reproduce the conditions.

This isolation lets reviewers attribute a result to the tested temperature and release it with greater confidence. If a prompt improvement lands mid-test, restart the comparison rather than mixing observations from two prompt versions in the same evaluation set.

3. Run representative utterances

Take a representative set of recorded customer utterances for one intent and run it at two or three settings. Cover the accents, phrasings, and edge cases that appear in production traffic, not just the clean examples used during development. Score every output on policy adherence and wording quality, and record any turn where the AI agent should have handed off but did not.

Representative inputs expose production risks before customers hear them, and they give the test team defensible evidence when a stakeholder questions the chosen band.

4. Score and release the setting

Record escalation behavior separately, including whether the AI agent handed off when it should have and whether it invented a policy detail. The test team rejects any band that produces a single off-policy fee statement in the set, regardless of how natural the rest sounds, because a fabricated fee is a compliance event rather than a stylistic preference.

Adopt the surviving setting for that intent and record it with the test results before moving to the next intent. If two bands pass, prefer the lower one, because it narrows the distribution of future outputs and shortens the review workload once the intent is in production.

5. Govern the setting through versioned releases

A documented temperature-governance process keeps the setting auditable long after the person who chose it has moved on.

• Log the temperature value for every production AI agent beside the prompt template, model version, and the input-output pairs it produced during acceptance testing.

• Assign a named owner and release every change as a version, with the date and reason recorded and the supporting tests attached.

• Treat a model upgrade as a new version in the same log.

In a regulated voice deployment, link the call transcript to the logged configuration with the same call identifier so a reviewer can explain the variance in two cancellation-fee answers.

6. Adjust the process for voice

Voice AI agents answer within the rhythm of a live phone conversation, so teams have less time than in chat to filter an output before the customer hears it. Any moderation step must be completed before speech generation without disrupting the conversation, so escalation logic should run independently of temperature rather than depending on a post-hoc filter. Do not use prompting to compensate for a mis-set temperature by piling on instructions; retest the setting instead.

For AI agents covering multiple languages, define the representative utterance coverage and sample size for every supported language, because a band validated in one language rarely transfers unchanged.

Govern LLM temperature settings in production

Temperature is a sampling control, not a quality dial, and treating it as one is what turns a defensible configuration into an unexplained transcript. The teams that keep customers safe are the ones that test each intent separately, log every value alongside the model and prompt that produced it, and reserve grounding and guardrails for the problems temperature cannot solve. That discipline is what makes an incident recoverable rather than reputational.

Parloa is and AI Agent Management Platform designed to support governed AI agent testing and configuration across Build, Optimize, and Observe, with per-intent temperature control, versioned releases, and linked transcript-to-configuration audit trails. Compliance coverage includes ISO 27001:2022, ISO 17442:2020, SOC 2 Type 1 & 2, PCI DSS, HIPAA, and DORA, so regulated deployments inherit the controls their auditors expect.

Book a demo to govern temperature changes from testing through production. Customers will not remember the setting behind an answer; they will remember how the company corrected a mistake.

FAQs about LLM temperature

Does the same temperature setting behave the same way across different models?

No. Temperature rescales each model's own probability scores, and the same number can produce different amounts of variance on different models. Rerun the comparison test after any switch of model or model version so the production setting reflects the current model.

Who should own the temperature setting in an enterprise deployment?

The person accountable for the AI agent's production behavior should own the setting. Engineering should execute each change as a versioned release, so every value carries a name, a date, and a reason.

Does a higher temperature cause more hallucinations?

A higher temperature can increase unsupported variation in some tasks because it flattens the token-probability distribution and makes lower-ranked tokens more likely.

Confabulation depends on grounding and controls beyond temperature. Retrieval and guardrails address fabricated fees caused by missing or wrong grounding.

Can temperature change during a single conversation?

Yes, if each intent or turn type carries its own configuration. An authentication step and a follow-up small-talk turn can run at different values inside one call, as long as the system logs each value against the applicable turn.

What temperature should a customer service AI agent use?

Start with roughly 0 to 0.3 for policy, payment, authentication, and routing turns, and use a moderate band for open conversation. Treat both ranges as hypotheses until a side-by-side test on real utterances confirms them, then retest after a model version change, prompt change, or new language rollout.