Temperature
A parameter controlling AI output randomness. Low temperature (0.1-0.3) produces predictable, focused text. High temperature (0.7-1.0) produces creative, varied outputs.
Why it matters
Temperature is the one dial that most changes AI output feel, and many tools expose it. It matters because the right setting depends on your task: you want boring reliability for facts and code, but variety and surprise for brainstorming or creative writing. Knowing it exists explains why the same prompt can give identical answers one day and wildly different ones another.
In practice
Ask for a company slogan at low temperature and you'll get safe, predictable lines, often the same one repeatedly. Crank it up and you'll get quirky, unexpected options worth stealing ideas from. Flip it: for "extract the total from this invoice," you want low temperature so it doesn't get creative with your numbers. Match the dial to whether you need consistency or imagination.
Related terms
AI Dubbing
Automated translation and re-voicing of audio/video content into other languages while preserving the original speaker's voice characteristics, timing, and emotional delivery.
AI Orchestration
Coordinating multiple AI models, tools, and data sources in a unified pipeline. An orchestration layer manages prompt routing, context passing, error handling, and output aggregation across different AI services.
API (Application Programming Interface)
A way for software applications to communicate with each other. AI APIs let developers integrate AI capabilities into their own applications programmatically.
Attention Mechanism
A technique that allows AI models to focus on the most relevant parts of input data when generating output. In language models, attention determines which words in a sentence are most important for understanding each other word.
Diffusion Model
A type of generative AI that creates images by gradually removing noise from a random starting point. Powers tools like Stable Diffusion, DALL-E, and Midjourney. Works by learning the reverse of a noise-adding process.
Distillation
A technique where a smaller 'student' model learns to replicate the behavior of a larger 'teacher' model. Produces compact models that retain most of the teacher's capability while being faster, cheaper, and deployable on smaller devices.