Controlling Creativity with Model Parameters
Learn how to tune model parameters to precisely control the creativity, predictability, and structure of AI outputs.
Imagine we are building an intelligent marketing co-pilot, an AI tool designed to assist a marketing team. This tool has two primary functions:
Its primary function is to perform precise analysis. When given a competitor’s web page, it must parse the content and extract a list of product names and their exact prices. This task requires strict consistency. If two people analyze the same web page, the output must be identical each time.
The tool’s second function is to support creative idea generation. Using the extracted product names, it must generate a set of varied slogan options for a new advertising campaign. This task requires a degree of variation in the generated outputs. If the tool produces the same slogans repeatedly, it does not meet the requirements of this creative task.
This presents a core engineering challenge. We are asking the same underlying AI model to handle two tasks with competing requirements: one that requires deterministic precision and another that depends on high variability and creative output. Our prompts for each task might be perfectly written, but the text alone cannot force this behavioral switch. How do we command the model to be a machine in one moment and a muse in the next?
The answer lies beyond the words we write, in a second, equally powerful lever of control: model parameters. These are specific settings that we can adjust during an API call, directly influencing how the model generates its response. They are the control panel for the AI’s generation engine.
Exploring these parameters is what elevates us from simply writing prompts to truly engineering AI behavior. Let’s explore the key model parameters that allow us to precisely control an AI’s creativity, randomness, length, and structure, transforming it from an unpredictable oracle into a reliable and predictable engineering tool.
The creativity parameters: Controlling randomness and diversity
To effectively use the parameters that control creativity, we first need a simple but accurate mental model of the text generation process.
At each step of generating a response, an LLM does not simply pick the next word. Instead, ...