Definition
Parameters are internal variables in AI model that have influence on how it behaves and the output it gives as results. They are just like control settings, such as knobs and switches, that the model can adjust to improve its performance. These parameters are not set manually; instead, they are learned automatically during the training process. While training the model, different inputs are fed to it, with its parameters also adjusted accordingly to reduce errors in its predictions [1].
For instance, imagine a Lagos clothing store using a predictive model to forecast apparel sales. This model factors in garment pricing, social campaigns, and fashion cycles. Trained with historical purchase data, it learns from optimal parameters to predict future demand accurately. Usually, the better the variables, the more precise the forecasts, enabling informed decisions about inventory levels, staff scheduling, and promotional strategies.
How AI models use parameters to make predictions [2]
Origin
The origin of the concept of model parameters can be traced back to the early stages of machine learning and statistical modeling. The use of parameters as settings in models has been a vital part of AI's development and its related fields. Over the course of time, developmental strides in AI research and the substantial growth of available data have led to significant progress in the understanding and optimization of model parameters [3].
Context and Usage
AI model
parameters have so many applications that cuts across multiple domains. Some of
them are as follows;
- Customer Service Chatbots: For handling common queries in customer support.
- Text Classification: Such as spam detection in emails.
- Healthcare Diagnostics: For tasks like symptom checking or treatment recommendations.
- Supply Chain Optimization: For automating and optimizing logistics.
Why it Matters
Parameters matter greatly in AI. They are the variables that the model learns from during training, and they have impact on the model's performance. The quality of the learned parameters can greatly affect the model's ability to make accurate predictions or decisions [4].
In Practice
A good example
of a real-life case study of parameters been put into practice can be seen in
the case of GPT. Generative Pre-trained Transformer, popular known by its
abbreviation GPT, is a series of large language models (LLM) developed by
OpenAI that have significantly influenced both the ML and AI fields.
GPT, at its core, is designed to understand and generate human-like text based on the input it receives. These models are trained from vast datasets. The GPT family of models has been instrumental in popularizing LLM-based applications, setting new benchmarks for what is possible in natural language processing, generation, and beyond [5].
See Also
Related Model
Training and Evaluation concepts:
- Overfitting: Problem where a model learns training data too well and fails to generalize to new data
- Presence Penalty: Parameter that reduces repetition by penalizing tokens that have already appeared
- Pre-Training: Initial training phase where models learn general patterns from large datasets
- Prompt: Input text or instruction given to an AI model to generate a response
- Prompt Engineering: Craft of designing effective prompts to get desired AI responses
References
- Cser, T. (2024). Understanding Tokens and Parameters in Model Training: A Deep Dive.
- Matt, B. (n.d). What are AI parameters?
- Lark Editorial Team. (2023). Parameters.
- TEDAI San Francisco. (2025). Parameters.
- Ferrer, J. (2025). Every thing We Know About GPT-5.