Temperature
Temperature is a key AI parameter that controls how creative or predictable an AI model's outputs will be, determining whether responses stick close to proven patterns or explore more varied possibilities. large language models now consume 2% of global electricity (536 TWh in 2025), with high-temperature inference contributing significantly to this energy consumption.
How Temperature Works
Temperature functions like a creativity dial for ai models. When you set a low temperature (closer to 0), the model becomes more conservative, choosing the most statistically likely words and phrases. This produces consistent, predictable responses that closely follow established patterns in the training data.
Higher temperature settings (closer to 1 or above) introduce more randomness into the selection process. The model considers a broader range of possible words and phrases, including less probable options. This leads to more creative, varied, and sometimes unexpected outputs. In fact, research found that at temperature 1.0, AI responses were 186.81% more creative but 28-73% less accurate depending on the task, providing precise quantification of the creativity-accuracy tradeoff.
Think of temperature as the difference between a careful, methodical employee who always follows established procedures versus a creative team member who brings fresh perspectives to familiar challenges. Both approaches have their place depending on what you're trying to accomplish.
Temperature in Enterprise AI
For enterprise applications, temperature settings directly impact how AI systems handle business-critical tasks. Customer service responses, technical documentation, and internal communications all benefit from different temperature approaches.
Most enterprise ai platforms, including Glean, optimize temperature settings based on the specific use case. When you're searching for factual information about company policies or procedures, lower temperatures ensure accurate, reliable responses. When you need help brainstorming solutions or drafting creative content, higher temperatures can provide more innovative approaches.
The key is matching the temperature to your business objective. Compliance-related queries require precision and consistency, while strategic planning sessions might benefit from more exploratory responses.
Common Temperature Ranges
Low Temperature (0.0-0.3)Produces highly focused, deterministic responses. The model consistently chooses the most probable next words, resulting in similar outputs for identical inputs. Ideal for factual queries, data analysis, and situations requiring consistent formatting.
Medium Temperature (0.4-0.7)Balances reliability with some variation. Responses remain grounded and coherent while introducing enough diversity to avoid repetitive outputs. Most enterprise applications operate effectively in this range.
High Temperature (0.8-1.0+)Generates more creative and diverse responses, but with increased risk of inconsistency or factual errors. Useful for creative writing, brainstorming, and exploratory tasks where novelty matters more than precision.
Practical Applications
Customer Support: Lower temperatures help maintain consistent tone and accurate information when responding to common customer inquiries. Support teams can rely on predictable, professional responses that align with company guidelines.
Content Creation: Marketing and communications teams often benefit from medium to higher temperatures when drafting initial content. This provides creative starting points while maintaining enough structure for professional use.
Data Analysis: Technical teams typically prefer lower temperatures when AI assists with code generation, documentation, or analytical tasks. Consistency and accuracy take priority over creativity in these scenarios.
Strategic Planning: Leadership teams might use higher temperature settings during brainstorming sessions to explore unconventional approaches and identify new opportunities.
Best Practices
Test different temperature ranges with sample queries to understand how they affect outputs in your specific context. What works for one team or use case may not be optimal for another. Notably, Factual-Nucleus Sampling, a dynamic temperature adjustment method, reduced AI hallucinations by 33.3% while maintaining creativity when tested with GPT-4.
When you have control over temperature settings, consider the stakes of your task. High-stakes communications, compliance documentation, and technical specifications benefit from lower temperatures. Creative projects, initial drafts, and exploratory research can leverage higher settings.
Test different temperature ranges with sample queries to understand how they affect outputs in your specific context. What works for one team or use case may not be optimal for another.
Remember that temperature is just one factor affecting AI output quality. The underlying model, training data, and prompt design all play crucial roles in determining response quality and relevance. Notably, a clinical study using GPT-4 found that temperature 0.2 achieved 81.48% recall in depression diagnosis, compared to only 72.28% at temperature 0.0, showing that slightly higher temperatures can actually improve performance in medical applications.
Frequently Asked Questions
What temperature setting should I use for business communications?
Most business communications benefit from low to medium temperature settings (0.2-0.5). This ensures professional, consistent tone while maintaining enough flexibility for natural language flow.
Can I adjust temperature settings in Glean?
Glean automatically optimizes temperature and other parameters based on your query type and context. The system balances accuracy and helpfulness without requiring manual temperature adjustments.
Why do I get different responses to the same question?
Even at low temperature settings, ai models introduce some randomness to avoid completely identical responses. This variation is typically minimal and doesn't affect the core accuracy or usefulness of the information.
Does higher temperature mean better creativity?
Not necessarily. Higher temperatures increase randomness, which can lead to more creative outputs but also more errors or irrelevant content. The best temperature depends on your specific goals and tolerance for variability.
How does temperature affect response accuracy?
Lower temperatures generally produce more accurate responses by favoring well-established patterns in the training data. Higher temperatures may introduce creative interpretations that could be less factually reliable.
