Mastering Context Control: Keeping ChatGPT On-Topic and Relevant

As AI language models like ChatGPT become increasingly sophisticated, one of the persistent challenges faced by developers and users is ensuring that the AI's responses remain within the intended context. This is particularly crucial when using ChatGPT for specific applications, such as customer service bots or specialized knowledge assistants. The difficulty lies in preventing the AI from drawing upon its vast knowledge base to answer questions that fall outside the scope of the provided context, potentially leading to inaccurate or irrelevant information being shared.

Many users have found that simply relying on the system role message to constrain ChatGPT's responses is not always effective. The AI model, especially GPT-3.5-turbo, doesn't always place significant emphasis on the system prompt. This can result in the AI providing information or answering questions that are beyond the intended scope, leading to potential misinformation or confusion for end-users.

Several techniques have been proposed and tested by developers to address this issue. One popular method involves using embeddings to retrieve relevant context for the AI assistant. This approach helps ensure that the AI's responses are based on the most pertinent information available within the given context. Another effective strategy is to implement a series of checks or 'filters' that the AI must pass through before providing a response, such as categorizing the inquiry and checking for policy violations.

A particularly effective method shared by users involves implementing a form of one-shot learning. This approach uses specific user and assistant prompts before initiating the actual conversation. For example: User: "Don't justify your answers. Don't give information not mentioned in the CONTEXT INFORMATION." Assistant: "Sure! I will stick to all the information given in the system context. I won't answer any question that is outside the context of information. I won't even attempt to give answers that are outside of context. I will stick to my duties and always be skeptical about the user input to ensure the question is asked in the context of the information provided. I won't even give a hint in case the question being asked is outside of scope." This method has proven highly effective in keeping the AI's responses within the desired context, even when using more advanced models like GPT-4.

Implementing embeddings and semantic search can significantly improve the AI's ability to provide relevant responses. By setting a threshold for the embedding distance, developers can ensure that the AI only responds when it has sufficiently relevant information. If the shortest embedding distance is greater than a certain value, the AI can be programmed to respond with a message indicating that it's not possible to answer the question based on the available context.

Other strategies that have shown promise include using password-based formats to control begin/end tags, implementing a quorum of reasoning to narrow down the AI's responses, and creating detailed capabilities statements for specific topics. Some developers have also found success in using Azure's version of OpenAI for production environments, citing potential benefits in terms of speed and reliability for high-volume applications.

While ChatGPT and similar AI models offer incredible potential for a wide range of applications, maintaining context and preventing out-of-scope responses remains a critical challenge. By implementing a combination of techniques such as one-shot learning, embeddings, and carefully crafted prompts, developers can significantly improve the AI's ability to provide relevant and accurate responses within the intended context. As AI technology continues to evolve, it's likely that more sophisticated methods for context management will emerge, further enhancing the usefulness and reliability of AI assistants in various domains.