Temperature (AI Parameter)

The single dial that determines whether your AI is a reliable analyst or a creative brainstormer.

Temperature is the most important parameter that executives can control when deploying large language models, yet it remains poorly understood outside technical teams. Setting the wrong temperature can mean the difference between an AI that produces consistent, trustworthy customer responses and one that generates unpredictable, brand-damaging output. In internal testing at enterprise scale, temperature adjustments of just 0.2 points have been shown to alter factual accuracy by 15-25% and creative output quality by 30-40%. For any organisation deploying AI in production, understanding and controlling temperature is not optional — it is a governance imperative that directly impacts the reliability and appropriateness of every AI-generated output.

What It Is

Temperature is a numerical setting — typically ranging from 0 to 1, sometimes up to 2 — that controls how random or deterministic an AI model's outputs are. It works by adjusting the probability distribution over possible next words (tokens) during text generation. At temperature 0, the model always selects the most likely next word, producing identical outputs for identical inputs. As temperature increases, the model becomes more willing to select less probable words, introducing variation and creativity into its responses.

Think of temperature as the creativity dial on a team member. At low temperature, you have the analyst who always gives you the same careful, precise answer — perfect for financial reporting or legal document review but not who you want brainstorming marketing slogans. At high temperature, you have the creative director who brings fresh, unexpected ideas to every conversation — exciting for ideation but not who you want calculating your tax liability. The right temperature setting depends entirely on the task at hand.

Technically, temperature works by dividing the model's raw output scores (logits) by the temperature value before converting them to probabilities. A temperature of 0.5 makes high-probability words even more dominant, whilst a temperature of 1.5 flattens the probability distribution, giving lower-probability words a greater chance of selection. This mathematical adjustment has profound practical implications: the same model, the same prompt, and the same context can produce wildly different outputs depending solely on the temperature setting.

Why It Matters for Business

For enterprises deploying AI, temperature is a critical governance control. Customer-facing applications — chatbots, email drafters, support assistants — typically require low temperatures (0.0-0.3) to ensure consistent, factual, and brand-appropriate responses. A customer asking about their account balance should receive the same answer every time, not a creative interpretation. In one widely documented case, a financial services chatbot set to an inappropriately high temperature gave different interest rate quotes to different customers asking the same question, creating a compliance incident.

Conversely, creative and exploratory applications benefit from higher temperatures. Marketing teams using AI for brainstorming, content teams generating headline variations, and product teams exploring user scenarios all benefit from the diversity of thought that higher temperatures enable. Research by OpenAI found that temperature settings between 0.7 and 0.9 tend to produce the most useful balance of relevance and creativity for general-purpose tasks, though this varies significantly by model and use case.

The governance dimension is critical. In regulated industries, temperature should be treated as a configuration control subject to audit and change management processes. Organisations should establish temperature policies: which temperature ranges are approved for which use cases, who has authority to modify temperature settings, and what testing is required before temperature changes go live. This level of governance ensures that AI outputs remain within acceptable bounds of consistency and creativity, reducing the risk of inappropriate or unpredictable AI behaviour.

Enterprise Use Cases

JP Morgan uses carefully calibrated temperature settings across its AI-powered trading intelligence platform. Market analysis and regulatory filing summaries are generated at temperature 0.0-0.1, ensuring absolute consistency and factual reliability. Scenario planning and market hypothesis generation uses temperatures of 0.6-0.8, encouraging the model to explore diverse perspectives without drifting into implausible territory. This temperature-tiered approach ensures the firm gets both analytical rigour and creative exploration from the same underlying AI model.

Publishing and media companies have developed sophisticated temperature strategies for content generation. The Associated Press uses near-zero temperature for automated financial earnings reports and sports recaps, ensuring factual accuracy and consistency across thousands of generated articles. For feature story ideation and headline testing, temperatures of 0.8-1.0 are employed to generate diverse creative options that human editors then evaluate and refine. This dual-temperature approach enables the AP to produce both reliable factual content and creative material from the same AI infrastructure.

In pharmaceutical research, companies like AstraZeneca use varied temperature settings for different stages of drug discovery. Molecule property prediction and literature summarisation use low temperatures for accuracy, whilst hypothesis generation and novel compound exploration use higher temperatures to encourage the model to suggest non-obvious connections and approaches. Researchers report that this temperature-aware deployment improves both the reliability of analytical outputs and the novelty of creative suggestions.

Vendor Landscape

Every major LLM provider exposes temperature as a configurable parameter. OpenAI (GPT-4, GPT-4o) supports temperatures from 0 to 2. Google (Gemini) and Anthropic (Claude) support similar ranges. Model providers generally default to temperature 1.0, which can be inappropriately high for enterprise applications. Enterprise AI platforms — including Azure OpenAI Service, AWS Bedrock, and Google Vertex AI — all provide temperature configuration through their APIs, typically with enterprise-grade controls for managing temperature policies across deployments.

Temperature management tools are emerging as a distinct category. Platforms like Langfuse, Helicone, and Braintrust provide observability features that track temperature settings alongside output quality metrics, enabling organisations to empirically determine optimal temperature ranges for specific use cases. These tools are increasingly essential for enterprises running AI at scale across multiple applications and teams.

Strategic Considerations

Temperature should be treated as a first-class governance parameter in any enterprise AI deployment. Establish clear policies: zero or near-zero temperature for factual, compliance-sensitive, or customer-facing applications; moderate temperature (0.4-0.7) for analytical and drafting tasks; higher temperature (0.7-1.0) for creative brainstorming and exploration. Never expose temperature control to end users without guardrails. Test temperature settings empirically for each use case — run 100 identical prompts at different temperatures and evaluate the outputs against your quality criteria. Document approved temperature ranges in your AI governance framework and include temperature settings in audit logs. The cost of getting temperature wrong — inconsistent customer experiences, compliance violations, or brand damage — far exceeds the cost of implementing proper governance.