Method and system for dynamic guardrails framework with plug-in for large language models (llms)

A system for creating guardrails for Large Language Model (LLM), the system comprising:

• a memory storing instructions;
• one or more Input/Output (I/O) interfaces;
• and
• one or more hardware processors coupled to the memory via the one or more I/O interfaces, wherein the one or more hardware processors are configured by the instructions to:
• create a customized guardrails configuration file for an LLM application by setting a plurality of parameters for guardrails in accordance with use case specific requirements, the plurality of parameters for the guardrails specifying i) a set of validations, ii) a set of validation preferences, and iii) a set of thresholds and actions applicable for each validation of the set of validations;
• determine via an expert selection LLM i) a set of experts from among a plurality of experts in accordance with the customized guardrails configuration file, ii) a sequence of the set of experts to be executed, and iii) a set of input parameters comprising applicable thresholds and actions to each of the set of experts;
• generate a wrapper via a wrapper generator LLM using a seed prompt, the wrapper comprising a code with a set of function calls to the set of experts defined in an order and with associated actions and thresholds tagged to each expert of the set of experts;
• optimize the wrapper by revising the code via the wrapper generator LLM, to obtain a deliverable guardrail code by iteratively optimizing the seed prompt, wherein each iteration comprises:
• validating the wrapper for a set of predefined checks and a set of dynamically generated checks;
• detecting, on occurrence of failure a validation among the set of validations, a reason for failure of one or more of the set of predefined checks and the set of dynamically generated checks;
• converting the detected reason to a prompt by a reason to prompt converter LLM;
• optimizing the seed prompt, via an prompt optimizer LLM, in accordance with the prompt obtained from the reason;
• and
• optimizing the wrapper in accordance with the optimized seed prompt in each iteration until the set of predefined checks and the set of dynamically generated checks are cleared by the wrapper to obtain the deliverable guardrail code;
• and
• provide the deliverable guardrail code as a plug-in into the LLM application without modifying the LLM application by treating the LLM application as a black box.

One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause:

• creating a customized guardrails configuration file for an LLM application by setting a plurality of parameters for guardrails in accordance with use case specific requirements, the plurality of parameters for the guardrails specifying i) a set of validations, ii) a set of validation preferences, and iii) a set of thresholds and actions applicable for each validation of the set of validations;
• determining via an expert selection LLM i) a set of experts from among a plurality of experts in accordance with the customized guardrails configuration file, ii) a sequence of the set of experts to be executed, and iii) a set of input parameters comprising applicable thresholds and actions to each expert of the set of experts;
• generating a wrapper via a wrapper generator LLM using a seed prompt, the wrapper comprising a code with a set of function calls to the set of experts defined in an order, with associated actions and thresholds tagged to each expert of the set of experts;
• optimizing the wrapper by revising the code via the wrapper generator LLM, to obtain a deliverable guardrail code by iteratively optimizing the seed prompt, wherein each iteration comprises:
• validating the wrapper for a set of predefined checks and a set of dynamically generated checks;
• detecting, on occurrence of failure of a validation among the set of validations, a reason for failure of one or more of the set of predefined checks and the set of dynamically generated checks;
• converting the detected reason to a prompt by a reason to prompt converter LLM;
• optimizing the seed prompt, via a prompt optimizer LLM, in accordance with the prompt obtained from the reason;
• and
• optimizing the wrapper in accordance with the optimized seed prompt in each iteration until the set of predefined checks and the set of dynamically generated checks are cleared by the wrapper to obtain the deliverable guardrail code;
• and
• providing the deliverable guardrail code as a plug-in into the LLM application without modifying the LLM application by treating the LLM application as a black box.