Can OpenAI models learn from user input over time?

OpenAI models like GPT-3.5 and GPT-4 do not learn from user input over time in the traditional sense. These models are static once trained, meaning their internal parameters (weights) remain fixed after their initial training phase. When users interact with them, the models generate responses based on patterns learned during training, not from new information provided during conversations. For example, if a user corrects a model’s mistake in a chat session, the model won’t retain that correction for future interactions—each query is processed independently, without any persistent memory of past exchanges.

Developers can, however, simulate a form of “learning” by customizing how the model is used. For instance, applications can store previous interactions and include them as context in subsequent requests. This allows the model to reference earlier parts of a conversation within a single session, creating the illusion of continuity. A practical example is a chatbot that maintains a session history: if a user specifies they prefer summaries in bullet points, the app can append this instruction to future queries in that session. Additionally, fine-tuning—a process where the base model is retrained on specific datasets—enables organizations to adapt the model for niche tasks. For example, a medical app could fine-tune a model on clinical notes to improve its performance in that domain, but this requires deliberate effort and isn’t done automatically through user input.

The static nature of OpenAI models has implications for developers. Since the models can’t update dynamically, applications needing real-time adaptation must rely on external systems. For example, a customer support tool might combine GPT-4 with a database of updated product information, using the model to generate responses while pulling current data from the database. This separation also means developers must handle scenarios where the model’s knowledge is outdated (e.g., events post-2023 for GPT-4) by injecting relevant information into prompts. While this approach requires more engineering, it ensures control over data sources and avoids unintended behavior that could arise from uncontrolled model updates.