Machine Learning (ML) models use a parameter called temperature to control the randomness or creativity of their output.
Think of temperature as a “risk dial” — low values make the model play it safe, while high values let it take more chances in its word choices.
📊 Temperature vs. Output Behavior
| Temperature Range | ML Model Output Characteristics |
|---|---|
| 0.1 – 0.3 (Low) | • ✅ More predictable and factual • ✅ Deterministic responses |
| 0.7 (Medium) | • ⚖️ Balanced creativity and reliability • 🗣️ Natural and varied responses without being too random |
| 1.0 and above (High) | • ⚠️ Possible hallucinations • 🎲 High randomness and creativity |
🔎 Temperature Examples (Same Prompt, Different Temperatures)
Prompt:
What is Nmap?
The same prompt is given to the ML model at different temperature settings, and we see how the output changes based on the temperature.
Temperature: 0.2 (Very Low – Factual and Precise)
Response:
Nmap (Network Mapper) is an open-source tool used for network discovery and security auditing. It can identify hosts, services, operating systems, and vulnerabilities on a network.
This is very direct, informative, and accurate.
🔹 Temperature: 0.7 (Medium – Balanced and Natural)
Response:
Nmap is a powerful open-source tool used by system administrators and ethical hackers to map out networks. It helps identify what devices are running, what services they’re offering, and whether there are any open ports or vulnerabilities.
Still accurate, but more conversational and user-friendly.
🔹 Temperature: 1.0 (High – Creative and Metaphorical)
Response:
Think of Nmap as the digital equivalent of a flashlight in a dark server room—it scans the shadows of a network, revealing hidden machines, open doors, and the secrets lurking behind firewalls.
Metaphorical, imaginative, and a bit dramatic—less technical but engaging.