Personalized Feed With Dynamic LLM Prompts
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Solution Overview
Problem
Existing content feed systems lack personalization, providing users with irrelevant or overwhelming content that does not cater to their specific interests and preferences, leading to a suboptimal user experience.
Innovation Solution
A personalized feed system utilizing a large language model to rank content based on user profiles and generate unique, relevant pill prompts, which are dynamically displayed to users, allowing for interactive and context-specific information retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a traditional content feed system is used to provide content to users, then content delivery is simple and fast, but the content lacks personalization and relevance to user interests
Solution Approach 1:
The content feed is segmented into multiple personalized feeds based on user profiles, interests, and behavior patterns. Each user receives a customized content sequence rather than a single generic feed, allowing the system to deliver relevant content while managing complexity through modular user profile management.
Solution Approach 2:
User profiles, interest tags, and preference data are collected and processed in advance before content delivery. The system pre-analyzes user behavior patterns and pre-ranks content based on predicted user preferences, reducing real-time processing complexity while maintaining high personalization quality.
2Ease of operation
If all available content is presented to users, then content completeness is high, but user cognitive load increases and engagement decreases
Solution Approach 1:
The system extracts and highlights only the most relevant content elements from the vast available content pool based on user profiles and behavior analysis. By filtering out irrelevant content and presenting only high-value items, the system reduces content volume while maintaining engagement through personalized relevance.
Solution Approach 2:
Different portions of the content feed are optimized with varying levels of detail and personalization based on user preferences. High-priority content receives enhanced personalization and prominence, while lower-priority content is simplified or aggregated, creating a quality gradient that manages cognitive load while preserving important information.
3Ease of operation
If manual content access is required from content sources, then content accuracy is high, but user convenience and accessibility are reduced
Solution Approach 1:
The system introduces an intelligent intermediary layer that automatically retrieves, validates, and curates content from multiple sources based on user profiles. This intermediary maintains high content accuracy through verification processes while simultaneously improving accessibility by delivering content directly to users without manual navigation, preserving contextual information through structured data extraction.
Data Source
AI summary
The disclosed technology is generally directed to a personalized feed. In one example of the technology, selected key-value pairs from a profile associated with a user are provided. Based on a prompt that includes natural-language text instructions, the selected key-value pairs, and ranked content, a large language model is used to generate: pill prompts associated with the ranked content, such that the pill prompts are information requests that are unique and personalized to have particular relevance to the user based on selected key-value pairs, and a response to each pill prompt such that the response includes content corresponding to the requested information. A content feed is displayed to the user, including displaying selectable pills to the user as part of the displayed content feed such that each selectable pill includes a corresponding pill prompt. The response to the pill prompt that corresponds to the selection is displayed to the user.