Preventing ChatGPT from Straying: Using 'Post-Prompts' for Context Control

The challenge of controlling AI models like ChatGPT to stay within the bounds of provided context is a common concern for developers. This article explores a practical solution using a 'post-prompt' technique to prevent ChatGPT from answering questions outside the scope of the given information.

One of the biggest hurdles in using ChatGPT for specific tasks, such as customer support or information retrieval, is its tendency to answer questions unrelated to the provided context. This can lead to irrelevant or inaccurate responses, undermining the usefulness of the AI assistant. Furthermore, ChatGPT might provide information from its general knowledge, which could include promoting competing products or services, a significant issue for businesses.

After extensive experimentation, a developer discovered that adding specific instructions after the user's query, known as a 'post-prompt', significantly improves ChatGPT's adherence to the provided context. This simple yet effective technique involves appending sentences like 'Don't justify your answers. Don't give information not mentioned in the CONTEXT INFORMATION' to the user's prompt.

The 'post-prompt' acts as a direct command to ChatGPT, limiting its response to only the information contained within the provided context. By explicitly instructing the AI not to draw on its general knowledge or provide extraneous details, the 'post-prompt' effectively constrains the scope of the response, ensuring relevance and accuracy.

Consider the scenario where a user asks ChatGPT to 'Prepare 10 multiple-choice questions and answers by the course of Maintenance engineering for apparel machinery.' Without the 'post-prompt,' ChatGPT might attempt to generate questions based on its general knowledge of maintenance engineering. However, with the 'post-prompt' in place, ChatGPT will respond with 'Sorry, I’m afraid I cannot fulfill that request as the provided CONTEXT INFORMATION does not relate to the topic of Maintenance engineering for apparel machinery,' effectively acknowledging the lack of relevant context.

The initial 'post-prompt' may require adjustments to suit different scenarios and contexts. For example, a more direct order like 'Do not give me any information about procedures and service features that are not mentioned in the PROVIDED CONTEXT' might be more effective in certain cases. Experimentation and refinement are key to optimizing the 'post-prompt' for specific applications.

While the 'post-prompt' technique has proven effective, developers are encouraged to explore alternative approaches and improvements. Sharing insights and experiences within the developer community can lead to further refinements and more robust solutions for controlling ChatGPT's responses.

By implementing a 'post-prompt,' developers can effectively control ChatGPT's responses, ensuring that the AI assistant stays within the bounds of the provided context. This technique is a valuable tool for building reliable and accurate AI-powered applications, particularly in scenarios where specific and limited information is required.