Natural-Language AI Queries with Multi-Constraint Guardrails
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Solution Overview
Problem
Large language models (LLMs) often provide inaccurate, inappropriate, or hallucinated responses due to their training on vast amounts of unstructured data, which limits their deployment in professional and sensitive contexts, and existing systems struggle to enforce multiple domain-specific constraints effectively.
Innovation Solution
A method involving an orchestrator that prepares a dataset, uses guardrail AIs to enforce domain-specific constraints, and modifies queries to ensure responses from a general-purpose LLM are relevant and appropriate, employing a multi-constraint prompt system to filter responses through guardrail LLMs for compliance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a general-purpose LLM is used to answer queries, then the model can interact with users in a natural way, but the responses may be inaccurate, inappropriate, or hallucinated
Solution Approach 1:
The patent introduces guardrail LLMs as intermediary components between the user query and the general-purpose LLM. These guardrail models evaluate whether the LLM's responses meet domain-specific constraints and filter out inappropriate or inaccurate information before it reaches the user, thus maintaining natural interaction while improving reliability
Solution Approach 2:
The system implements a feedback mechanism where guardrail LLMs continuously evaluate the responses generated by the general-purpose LLM against predefined constraints. When responses fail to meet the constraints, the system provides feedback by rejecting or modifying those responses, ensuring that only compliant information is delivered to users
2Reliability
If multiple domain-specific constraints are enforced on LLM responses, then response reliability improves, but system complexity increases
Solution Approach 1:
The patent segments the constraint enforcement function into multiple specialized guardrail LLMs, each responsible for evaluating specific domain-specific constraints. This segmentation allows the system to manage complexity by dividing the overall evaluation task into smaller, specialized components rather than using a single monolithic constraint checking system
Solution Approach 2:
The guardrail LLMs serve multiple functions: they evaluate domain-specific constraints, filter inappropriate responses, and ensure compliance with organizational policies. This multi-functionality reduces the need for separate specialized components for each constraint type, thereby managing system complexity while maintaining comprehensive constraint enforcement
Data Source
AI summary
There is disclosed a computer-implemented method, including receiving, from a human user, a natural language query; modifying the natural language query (a modified query) and posting the modified query to a general-purpose artificial intelligence (AI); receiving, from the general-purpose AI, a raw response to the modified query; providing the raw response to a plurality of guardrail AIs, wherein the guardrail AIs are to provide domain-specific evaluations of the raw response; receiving, from the plurality of guardrail AIs, the domain-specific evaluations; forwarding a version of the raw response to the human user; and acting on the domain-specific evaluations.