Abstract: A system for use with an artificial intelligence (AI) model configured to accept text input, such as generative pre-trained transformer (GPT), that detects and tags trusted instructions and nontrusted instructions of an input provided by a user responsive to an AI model prompt. The system uses reinforcement learning (RL) and a set of rules to remove the untrusted instructions from the input and provide only trusted instructions to the AI model. The input is represented as tokens, wherein the trusted instructions and the untrusted instructions are represented using incompatible token sets.

Abstract: A system and method of the injection of branding, ads, and helpline contact info based on user input into the content that is generated by general-purpose AI systems. A system and method allows brands, clients, companies, etc., to elicit desired behavior from general-purpose generative AI processes (chatbots, image generation AI, recommendation systems, audio systems, augmented reality, Virtual reality, mixed reality . . . ). Based on user input, a trigger is activated based on an input metric, and then the generative system modifies the input, creates a kind of expected behavior, etc.

Abstract: A system for use with an artificial intelligence (AI) model configured to accept text input, such as generative pre-trained transformer (GPT), that detects and tags trusted instructions and nontrusted instructions of an input provided by a user responsive to an AI model prompt. The system uses reinforcement learning (RL) and a set of rules to remove the untrusted instructions from the input and provide only trusted instructions to the AI model. The input is represented as tokens, wherein the trusted instructions and the untrusted instructions are represented using incompatible token sets.

Abstract: A system and method of the injection of branding, ads, and helpline contact info based on user input into the content that is generated by general-purpose AI systems. A system and method allows brands, clients, companies, etc., to elicit desired behavior from general-purpose generative AI processes (chatbots, image generation AI, recommendation systems, audio systems, augmented reality, Virtual reality, mixed reality . . . ). Based on user input, a trigger is activated based on an input metric, and then the generative system modifies the input, creates a kind of expected behavior, etc.

Abstract: A system for use with an artificial intelligence (AI) model configured to accept text input, such as generative pre-trained transformer (GPT), that detects and tags trusted instructions and nontrusted instructions of an input provided by a user responsive to an AI model prompt. The system uses reinforcement learning (RL) and a set of rules to remove the untrusted instructions from the input and provide only trusted instructions to the AI model. The input is represented as tokens, wherein the trusted instructions and the untrusted instructions are represented using incompatible token sets.

Abstract: A system for use with an artificial intelligence (AI) model configured to accept text input, such as generative pre-trained transformer (GPT), that detects and tags trusted instructions and nontrusted instructions of an input provided by a user responsive to an AI model prompt. The system uses reinforcement learning (RL) and a set of rules to remove the untrusted instructions from the input and provide only trusted instructions to the AI model. The input is represented as tokens, wherein the trusted instructions and the untrusted instructions are represented using incompatible token sets.