Digital Twin Prompts to get you going
This guide provides a collection of sample prompts for use in the
Digital Twin to help you get started.
The Digital Twin leverages advanced AI algorithms to create realistic and dynamic simulations of real-world entities, enabling users to explore various scenarios and outcomes.
The sample prompts are designed to guide users in generating accurate and meaningful digital twins, offering insights into the potential applications of the technology in fields such as customer analyses, marketing, sales, and product development.
Temperature
Temperature in a language model (LLM) is a setting that controls the randomness of its responses. It influences how creative or predictable the model's output is.
Low Temperature (e.g., 0.1 or 0.2): When the temperature is low, the model's responses are more focused and deterministic. It tends to choose the most likely next word, resulting in more predictable and sensible outputs. This is useful when you want clear and straightforward answers.
High Temperature (e.g., 0.8 or 1.0): When the temperature is high, the model's responses become more diverse and creative. It has a higher chance of picking less likely next words, leading to more varied and imaginative outputs. This is useful when you want creative writing or brainstorming.
In essence, temperature adjusts the level of creativity in the model's responses. Lower temperatures make the model's responses more conservative and predictable, while higher temperatures make them more varied and creative.