Enterprise LLM Query Filtering With Relevance Threshold Gating
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Solution Overview
Problem
Large language models (LLMs) in enterprises often process irrelevant queries, tying up valuable computing resources that could be used for more relevant tasks, due to their resource-intensive nature.
Innovation Solution
Implement a machine learning model to filter queries based on relevance to an enterprise's domain, generating relevance scores and taking remedial actions for queries that do not meet a threshold, ensuring only relevant queries are processed by the LLM.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If LLM processes all incoming queries, then comprehensive query handling is achieved, but computational resources are wasted on irrelevant tasks
Solution Approach 1:
The system performs preliminary classification of queries using a machine learning model before they reach the LLM. This preliminary action identifies and filters out irrelevant queries, ensuring that only relevant queries are processed by the resource-intensive LLM, thus preventing computational resource waste while maintaining productivity
Solution Approach 2:
A machine learning classification model is introduced as an intermediary between the query input and the LLM. This intermediary evaluates queries and determines relevance, acting as a gatekeeper that directs only appropriate queries to the LLM, thereby resolving the contradiction between comprehensive processing and resource efficiency
2Reliability
If LLM handles all queries including irrelevant ones, then no queries are lost, but resource utilization deteriorates
Solution Approach 1:
The query processing system is segmented into two distinct components: a machine learning classification model for initial relevance assessment and the LLM for detailed processing of relevant queries only. This segmentation allows the system to maintain reliability by handling all queries through the classification stage while improving resource utilization by restricting LLM processing to relevant queries
Solution Approach 2:
The system changes the parameter of query routing by introducing a relevance threshold parameter. Queries are evaluated against this parameter, and only those meeting the threshold are passed to the LLM. This parameter-based filtering maintains completeness of query handling while optimizing computational resource utilization
Data Source
AI summary
Techniques for filtering queries to a large language model (LLM) based on their relevance to an enterprise domain associated with the LLM involve training a machine learning model using historical LLM query data and associated relevance scores. These scores indicate how closely a query relates to the enterprise's operations. The trained model is then applied to new input queries, generating relevance scores for the input queries. Queries meeting a predetermined relevance threshold are passed to the LLM for processing. For queries falling below this threshold, remedial actions are taken instead of processing by the LLM. The techniques optimize computational resource allocation by prioritizing queries relevant to the enterprise while filtering out less pertinent ones. The techniques create a relevance-based gatekeeping mechanism for LLM query processing, enhancing efficiency and focusing the LLM's capabilities on enterprise-specific tasks.