Retrieval Augmented LLM Content Recommendation System
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Solution Overview
Problem
Large language models (LLMs) generate responses that may not be relevant or accurate due to their limited knowledge based on training data, leading to suboptimal content recommendations.
Innovation Solution
The system generates an augmented prompt for the LLM by combining user contextual information with retrieved content items, using feature vectors to determine relevant content and providing this information to the LLM for improved content recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If large language models are used for content recommendations, then responses can be generated quickly and in human-like text, but the relevance and accuracy of recommendations deteriorate due to limited knowledge from training data
Solution Approach 1:
The patent introduces an intermediary retrieval system that acts as a mediator between the user query and the large language model. This intermediary component searches through a knowledge base to find relevant content items and their metadata, which are then used to augment the prompt given to the LLM. This mediator ensures that the LLM receives up-to-date and relevant information rather than relying solely on its training data, thereby improving recommendation accuracy while maintaining generation speed.
Solution Approach 2:
The patent performs preliminary retrieval of relevant content items and their metadata before the LLM generates its response. By pre-fetching and preparing the necessary information from the knowledge base, the system ensures that the LLM has access to the most current and relevant data when generating recommendations, improving accuracy without adding significant delay to the overall process.
2Device complexity
If content recommendations are based solely on training data, then the system is simple to implement, but the knowledge is limited and may not be relevant to current user needs
Solution Approach 1:
The patent merges multiple components into a unified system: the large language model for generation, the retrieval system for information gathering, and the prompt augmentation mechanism for integration. This combination allows the system to leverage both the simplicity of LLM-based approaches and the information-rich nature of structured knowledge bases, achieving comprehensive recommendations without excessive complexity.
Solution Approach 2:
The retrieval system serves as an intermediary that bridges the gap between simple LLM operation and complex knowledge requirements. It automatically queries the knowledge base, filters relevant information, and prepares augmented prompts, thereby enriching the LLM's knowledge access without requiring complex manual curation or data processing on the part of the recommendation system itself.
3Measurement precision
If feature vectors are used to compare user context with content items, then relevant content can be accurately identified, but the computational requirements and processing time increase
Solution Approach 1:
The system performs preliminary computation of feature vectors for content items and stores them in advance. By pre-computing and caching these vectors, the system avoids the need to compute them on-demand during recommendation generation, significantly reducing processing time while maintaining high measurement precision through accurate vector-based similarity matching.
Data Source
AI summary
Systems, methods, apparatuses, and computer program products are disclosed for using retrieval augmented artificial intelligence to provide content recommendations. A first feature vector is generated based at least on user contextual information. Second feature vectors are determined based on a comparison of the first feature vector to a plurality of second feature vectors. Content items corresponding to the determined second feature vectors are retrieved. An augmented prompt generated based on the user contextual information and the retrieved content items is provided to a large language model to request a recommendation. A content recommendation is received from the large language model based on the augmented prompt.