Abstract: Accurate prompting improves the operation, efficiency, and output of computer models, such as language models. The disclosed systems and methods improve interaction with computer models by facilitating the generation of accurate prompts for targeted interactions with computer models. For example, disclosed systems can be configured to store prompts and responses generated with computer models in session memories. The system can use information in session memories to generate combined prompts when receiving prompts that refer to previous prompts or answers. The system improves prompt accuracy by generating combined prompts—formed by combining the context from the stored information (e.g., in context sub-prompts) and instructions in the new prompt (e.g., in instructions sub-prompts). The system can generate responses based on the combined prompts allowing the computer models to leverage the context in previous interactions, without burdensome or complicated prompts, for more meaningful or accurate responses.

Abstract: Accurate prompting improves the operation, efficiency, and output of computer models, such as language models. The disclosed systems and methods improve interaction with computer models by facilitating the generation of accurate prompts for targeted interactions with computer models. For example, disclosed systems can be configured to store prompts and responses generated with computer models in session memories. The system can use information in session memories to generate combined prompts when receiving prompts that refer to previous prompts or answers. The system improves prompt accuracy by generating combined prompts—formed by combining the context from the stored information (e.g., in context sub-prompts) and instructions in the new prompt (e.g., in instructions sub-prompts). The system can generate responses based on the combined prompts allowing the computer models to leverage the context in previous interactions, without burdensome or complicated prompts, for more meaningful or accurate responses.

Abstract: The present technology provides for the learning of information relevant to a user account by a generative response engine and accessing this information when preparing personalized responses to prompts provided by the user account. A further aspect of the present technology is that the user account does not need to explicitly tell the generative response engine to remember a particular information. Instead, the present technology is configured such that the generative response engine can learn such facts, preferences, or contexts from conversational prompts provided to the chatbot without providing explicit instructions to remember the data. A further aspect of the present technology is that the user account can request that the generative response engine forget some learned facts too.

Abstract: Accurate prompting improves the operation, efficiency, and output of computer models, such as language models. The disclosed systems and methods improve interaction with computer models by facilitating the generation of accurate prompts for targeted interactions with computer models. For example, disclosed systems can be configured to store prompts and responses generated with computer models in session memories. The system can use information in session memories to generate combined prompts when receiving prompts that refer to previous prompts or answers. The system improves prompt accuracy by generating combined prompts—formed by combining the context from the stored information (e.g., in context sub-prompts) and instructions in the new prompt (e.g., in instructions sub-prompts). The system can generate responses based on the combined prompts allowing the computer models to leverage the context in previous interactions, without burdensome or complicated prompts, for more meaningful or accurate responses.