AI Query Routing With Retrieval-Augmented Task Responses
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Solution Overview
Problem
Existing systems struggle to efficiently and accurately process natural language queries within organizational contexts, particularly in generating task execution strategies that align with predefined policies and historical data, leading to suboptimal decision-making and task execution.
Innovation Solution
A system utilizing a generative machine learning model, integrated with a classification model and a retrieval-augmented generation approach, processes natural language queries to classify query types and generate responses based on organizational policies and historical data, optimizing task execution strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a generative machine learning model with retrieval-augmented generation is used to process natural language queries, then the accuracy and relevance of task execution strategies are improved, but the system complexity increases
Solution Approach 1:
The system segments the query processing task into distinct components: a classification model that identifies query types (routing vs. general queries) and a generative model that generates responses based on the classified type. This segmentation allows each model to specialize in its specific function, improving overall accuracy while managing complexity through modular design.
Solution Approach 2:
The classification model serves as an intermediary between the user's natural language query and the generative model. It translates unstructured queries into structured query type classifications, which then guide the generative model in selecting appropriate response strategies. This intermediary layer improves precision by ensuring the generative model receives well-categorized input.
2Adaptability or versatility
If multiple operation modes (routing and query-response) are implemented based on query classification, then the adaptability of the system is improved, but the device complexity increases
Solution Approach 1:
The system implements dynamic operation modes that adapt based on the classified query type. For routing queries, the system activates a routing mode that directs to specific resources; for general queries, it activates a query-response mode that generates comprehensive answers. This dynamic adaptation allows the system to optimize its behavior for different query types while maintaining a unified interface.
Solution Approach 2:
The single system handles multiple types of queries (routing and general) through a unified architecture that automatically selects the appropriate operation mode. The classification model enables the system to universally process diverse query types without requiring separate dedicated systems, achieving multi-functionality while managing complexity through centralized control.
3Reliability
If retrieval-augmented generation is used to supplement the LLM with external data sources, then the reliability of responses is improved, but the loss of time in data retrieval increases
Solution Approach 1:
The system performs preliminary classification of the query type before initiating the retrieval-augmented generation process. By pre-identifying whether a query requires routing or general response generation, the system can optimize the retrieval process accordingly, reducing unnecessary data access operations and minimizing retrieval time while maintaining reliability for queries that require external data.
Data Source
AI summary
A disclosed method may include receiving, via a user interface, a natural language query that describes a task. The method may also include classifying, via a classification machine learning model, the natural language query as a query type included in a predetermined plurality of query types. The method may also include generating, by the processor via a generative machine learning model pre-trained to generate responses to queries based on at least one predetermined task execution policy, a response based on the natural language query and the query type. The method may also include providing, via the user interface, the response. Various other methods, systems, and computer-readable media are also disclosed.