Intermediary AI Routing And Moderation For Enterprise Queries
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Solution Overview
Problem
Generative AI systems often provide inaccurate responses due to inadequate training or contextual data, hallucinations, and varying costs and quality, making it difficult for users to efficiently obtain satisfactory answers.
Innovation Solution
A routing and moderation platform that includes an API for interfacing with multiple LLM-based generative AI systems, a prompt templating service for generating optimized prompts, and a moderation service for evaluating response quality, enabling enterprise-level management of queries and responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users submit queries to a particular generative AI platform and iterate questions to arrive at a desired answer, then the user can obtain satisfactory answers, but the approach is not efficient in terms of cost, completeness, accuracy, and time
Solution Approach 1:
The patent introduces an intermediary routing and moderation platform that sits between the user and multiple generative AI systems. This platform receives user queries, routes them to appropriate AI systems based on query characteristics and enterprise policies, moderates the responses for quality and accuracy, and presents the final answer to the user. This intermediary layer eliminates the need for users to manually iterate through different platforms, significantly improving query efficiency while maintaining answer quality through systematic routing and moderation.
Solution Approach 2:
The patent implements feedback mechanisms where the routing and moderation platform evaluates responses from generative AI systems based on quality metrics, accuracy assessments, and enterprise standards. This feedback loop allows the system to learn from previous interactions, improve routing decisions, and refine moderation criteria over time, thereby enhancing both answer quality and operational efficiency without requiring manual user iteration.
2Reliability
If larger LLM models are used to improve response accuracy and completeness, then the quality of answers improves, but the computational cost and subscription cost increase
Solution Approach 1:
The patent applies local quality by matching different types of queries to appropriately sized generative AI systems based on the specific requirements of each query. Complex queries requiring high accuracy and completeness are routed to larger, more capable models, while simpler queries are handled by smaller, more cost-effective models. This selective approach ensures that computational resources are allocated efficiently, with larger models used only when their enhanced capabilities are actually needed, thereby reducing overall computational costs while maintaining response accuracy.
Solution Approach 2:
The routing and moderation platform dynamically changes parameters such as model selection, prompt complexity, and processing depth based on query characteristics, enterprise policies, and cost considerations. This allows the system to adjust the level of computational resources applied to each query, using more sophisticated processing only when necessary, thereby optimizing the balance between response accuracy and computational cost across different query types.
3Reliability
If multiple generative AI systems are accessed to improve response quality, then answer accuracy improves, but the complexity of managing multiple systems increases
Solution Approach 1:
The patent merges multiple generative AI systems into a unified routing and moderation platform that manages all interactions with various AI models through a single interface. This consolidation allows the platform to orchestrate queries across multiple systems, aggregate and evaluate responses, and present unified answers to users, thereby improving response quality through diverse model inputs while reducing the complexity users would otherwise face in managing multiple systems independently.
Solution Approach 2:
The routing and moderation platform serves multiple functions: it routes queries to appropriate AI systems, manages authentication and access control, moderates responses for quality and compliance, evaluates answer accuracy, and presents results to users. This multi-functional design consolidates what would otherwise require separate systems for each function, reducing overall system management complexity while maintaining the ability to leverage multiple generative AI systems for improved response quality.
4Reliability
If prompts are made more detailed and contextualized to improve AI system understanding, then response accuracy improves, but the number of tokens increases
Solution Approach 1:
The routing and moderation platform applies partial action by selectively adding contextual information and detailed instructions to prompts based on the specific query requirements and the capabilities of the target AI system. Rather than always using maximum detail, the system adds only the necessary contextual elements needed to achieve accurate responses, thereby improving response accuracy while controlling token usage by avoiding unnecessary elaboration in cases where simpler prompts suffice.
Data Source
AI summary
A routing and moderation platform enables enterprise management of input queries and associated responses that may be submitted to and received from generative artificial intelligence (AI) systems. The routing and moderation platform includes an application programming interface (API) that enables an enterprise to interface with a plurality of different LLM-based generative AI systems. The API may route an input query and any related contextual information and additional instructions for forming a response to the input query to a selected LLM-based generative AI system, and receive a response in response thereto. The routing may be based, at least in part, on the particular source of a question and context of that question. The routing and moderation platform further includes a moderation service useable to quantify a quality of the response received from any of the generative AI systems accessed via the API.