Can I use OpenAI’s models for multi-turn conversations?

Yes, you can use OpenAI’s models for multi-turn conversations. Models like GPT-3.5-turbo and GPT-4 are designed to handle back-and-forth interactions by processing a sequence of messages in a structured format. Instead of treating each user input as an isolated prompt, you pass the entire conversation history—including prior user queries and model responses—to the API with each new request. This allows the model to maintain context and generate responses that align with the ongoing dialogue. For example, a developer building a customer support chatbot could send a list of messages (user questions and assistant replies) to the API, ensuring the model understands the flow of the conversation.

To implement this, you structure the input as a list of message objects, each specifying a role (e.g., “user,” “assistant,” or “system”) and content. The “system” role often sets the assistant’s behavior (e.g., “You are a helpful tutor”), while “user” and “assistant” messages represent the dialogue. For instance, in a travel planning app, the first message might be a system prompt like, “You help users plan trips by suggesting destinations and itineraries.” Subsequent messages would include the user’s requests (“I want to visit Japan in spring”) and the assistant’s prior responses (“Here’s a 7-day Tokyo itinerary…”). By including all messages in each API call, the model retains context, allowing it to answer follow-up questions like, “What about adding a day trip to Mount Fuji?”

However, there are practical considerations. First, each model has a maximum token limit (e.g., 4,096 tokens for GPT-3.5-turbo), so long conversations may require truncating or omitting older messages to stay within the limit. Developers often implement strategies like summarizing past interactions or prioritizing recent messages. Second, managing conversation state is the developer’s responsibility—the API doesn’t store prior interactions. Tools like LangChain or custom session management can help track messages. Finally, cost increases with more tokens, so optimizing conversation length is important. For example, a coding assistant might retain only the last five exchanges to balance context and efficiency. By carefully structuring inputs and managing tokens, developers can effectively use OpenAI’s models for dynamic, multi-turn applications.