LLM Dynamic Facets for Precise Content Filtering
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Solution Overview
Problem
Existing content management platforms suffer from inaccuracies and inefficiencies in filtering digital content due to the use of pre-defined filters that are either too broad or inaccurately tailored, leading to excessive navigation and operational inflexibilities.
Innovation Solution
A dynamic facet system that generates filters on-the-fly using a large language model, extracting raw facet data from content items, determining facet content groups, and creating a facet prompt to generate accurate and efficient dynamic facets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If pre-defined filters are used to narrow down content, then filtering capability is provided, but accuracy in locating specific content deteriorates due to filters being too broad or generic
Solution Approach 1:
The system dynamically generates filter facets on-demand based on the specific content collection being viewed, rather than using static pre-defined filters. This allows the filter criteria to adapt to the actual content characteristics, improving both the ease of operation and the precision of content location simultaneously
2Adaptability or versatility
If tailored filters are created based on digital content, then customization is provided, but accuracy deteriorates due to filters being created from generic information
Solution Approach 1:
The system enables self-service filter generation by automatically analyzing the content collection and generating appropriate filter facets without requiring manual configuration. The system serves itself by extracting meaningful attributes directly from the content, eliminating the need for generic pre-configured filters while maintaining high precision
3Reliability
If excessive navigation is required to locate content items, then filter accuracy is compromised, but time consumption and resource usage increase
Solution Approach 1:
The system performs preliminary action by pre-generating relevant filter facets based on the content collection before the user needs to search. This prepares the filtering mechanism in advance, allowing users to immediately access accurately filtered content without time-consuming navigation through multiple interfaces
4Device complexity
If rigid filter structures are used, then system simplicity is maintained, but operational flexibility deteriorates when handling diverse content volumes
Solution Approach 1:
The system achieves universality by creating a single dynamic filter generation mechanism that can handle diverse content collections of varying sizes and types. Rather than maintaining multiple rigid filter structures for different scenarios, one adaptive system provides operational flexibility across all content types while preserving simplicity through automated facet generation
Data Source
AI summary
The present disclosure is directed toward systems, methods, and non-transitory computer-readable media for generating a dynamic facet by using a large language model. For example, the disclosed systems extract raw facet data from a plurality of content items stored in a content management system. In addition, the disclosed systems determine one or more facet content groups by grouping the plurality of content items according to the raw facet data. Further, the disclosed systems generate a facet prompt from the one or more facet content groups. Moreover, the disclosed systems generate a dynamic facet by providing the facet prompt to a large language model.