What are parameters of AI models?
November 3, 2026 · 2 min read
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Parameters are numerical values that influence how artificial intelligence processes information, makes predictions, and generates responses. These numerical values are adjusted during training so that the model can identify patterns and improve responses. For instance, in chatbots like Gemini ChatGPT and Claude, parameters help AI relate words and calculate which tokens are most likely to appear next. A token can be a word, part of a word, or a punctuation mark. Based on these predictions, the model generates a response.
Parameters are internal values that the model learns from training data, determining which information is transformed into responses in AI chatbots. During training, the model makes predictions and compares these results with expected answers. The difference between them is called "loss." Optimization algorithms use this information to adjust parameters at various stages of training, enhancing the accuracy of predictions. When training concludes, parameters become fixed and can only be modified through new training.
A larger number of parameters can assist AI in learning complex relationships between data. In large language models like ChatGPT, Gemini, and Claude, these values help represent grammatical patterns, meanings, expressions, and relationships between different concepts. However, having more parameters does not necessarily mean having a better AI model. Quality also relies on the training data, choices made during development, and adjustments throughout the learning process.
The types of parameters vary according to the model's architecture. In neural networks, two main types are weights and biases, which work together to determine how information is processed. Weights define the influence of each input on the outcome, and biases are additional values that adjust the results of calculations regardless of the input data.
Parameters represent learned values during training, while the context window refers to the amount of information the model can consider in a task. This can include the command sent by the user, previous messages, and documents provided during the conversation. For example, when asking a chatbot to summarize a document, the AI analyzes the file to produce a response without permanently learning the content.
Parameters are the values learned during training, whereas hyperparameters are the settings that define how this learning occurs. Hyperparameters include learning rate, batch size, and number of epochs, which manage different stages of training.