A generalized model is an AI model designed to perform a broad range of tasks across various domains, without being tailored to any specific use case or industry. Unlike specialized models that are fine-tuned for particular applications (e.g., medical diagnostics or legal text analysis), a generalized model is built to handle multiple types of data and tasks, making it flexible and adaptable.
These models typically rely on large datasets that cover a wide array of topics or contexts, allowing them to generalize patterns and make predictions or generate outputs across different scenarios. However, because they are not fine-tuned for a specific task, their performance may not be as optimized as specialized models for certain applications. Generalized models are ideal when the goal is to solve a variety of problems or when the task requirements are not clearly defined upfront.