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AI and Machine LearningT

Temperature

Definition

Temperature is a parameter in language model inference that controls the probability distribution over the next token prediction. At temperature 0, the model always selects the most probable token, producing deterministic and repetitive outputs. As temperature increases toward 1.0 and beyond, the model distributes probability more evenly across tokens, producing more varied, creative, and sometimes surprising outputs.

Mathematically, temperature scales the logits (raw prediction scores) before they are converted to probabilities via the softmax function. A lower temperature sharpens the distribution, concentrating probability on the top choices. A higher temperature flattens it, giving less likely tokens a better chance of being selected. Most APIs allow temperature values between 0 and 2, with 1.0 as the default.

Why It Matters for Product Managers

Temperature is one of the most accessible and impactful parameters PMs can tune to control AI feature behavior. For factual tasks like answering support questions, extracting data, or generating structured outputs, low temperature (0.0 to 0.3) ensures consistent, reliable results. For creative tasks like brainstorming, writing marketing copy, or generating diverse suggestions, higher temperature (0.7 to 1.0) produces more varied and interesting outputs.

Choosing the wrong temperature creates immediate user experience problems. Too low on a creative feature and users see repetitive, boring suggestions. Too high on a factual feature and users get inconsistent or incorrect answers. PMs should treat temperature as a product design decision that gets tested and iterated on, not a technical detail left to engineering defaults.

How It Works in Practice

  • Classify the use case -- Determine whether the AI feature needs consistency (factual Q&A, data extraction, classification) or variety (brainstorming, creative writing, diverse recommendations).
  • Set an initial temperature -- Start with 0.0 to 0.2 for deterministic tasks, 0.5 to 0.7 for balanced tasks, and 0.8 to 1.0 for creative tasks. These are starting points, not final values.
  • Test with real inputs -- Run the same set of representative user queries multiple times at the chosen temperature. Evaluate whether the outputs are appropriately consistent or varied for the use case.
  • A/B test with users -- If the optimal temperature is unclear, run an A/B test with different temperature settings and measure user satisfaction, task completion, or other relevant metrics.
  • Consider dynamic temperature -- For products with multiple AI features, implement different temperature settings per feature or even per request type, rather than using a single global value.

    • Common Pitfalls

  • Using the default temperature (often 1.0) for all features without considering whether the use case needs consistency or creativity, leading to suboptimal user experiences.
  • Setting temperature to 0 for all features to maximize consistency, which eliminates useful variety in features that benefit from diverse outputs like brainstorming or content generation.
  • Confusing temperature with quality. Higher temperature does not make outputs better or worse; it makes them more varied. Quality depends on the model, prompt, and context.
  • Not testing temperature settings with real user inputs. The optimal temperature often differs from what feels right during internal testing because real user queries are more diverse and unpredictable.

    • Related Concepts

    Temperature is an inference parameter of Large Language Models (LLMs) that works alongside Prompt Engineering to shape output behavior. Higher temperature settings increase the risk of Hallucination by allowing less probable tokens, making temperature tuning critical for factual use cases.

    Related Terms

    Large Language Model (LLM)Prompt EngineeringHallucination

    Frequently Asked Questions

    What is temperature in product management?+
    Temperature is a parameter that controls how predictable or creative an AI model outputs are. For product managers, temperature is a critical configuration choice that determines whether an AI feature produces consistent, reliable responses (low temperature) or diverse, creative outputs (high temperature), directly shaping the user experience.
    Why is temperature important for product teams?+
    Temperature is important because it is one of the simplest and most impactful levers for tuning AI feature behavior. The right temperature setting can mean the difference between an AI assistant that gives reliable factual answers and one that produces creative but unpredictable responses. Product teams need to set temperature based on the specific use case requirements.
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