LLM Content Analysis Prompts for Low-Latency App Recommendations
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Solution Overview
Problem
Large language models (LLMs) introduce latency, require significant compute power, and often hallucinate, leading to inefficient user experiences and ineffective content generation due to the user's inability to effectively communicate with them.
Innovation Solution
Integrate LLM services within software applications to identify user content and context, generate prompts for LLMs to analyze content, and display recommendations for supplemental content, optimizing prompt size and interaction to enhance user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If LLM services are integrated for content analysis, then content generation capability is improved, but latency increases
Solution Approach 1:
The system performs preliminary actions by identifying and analyzing content and context locally before LLM processing. The application identifies content associated with the application and determines context relating to the content, preparing prompts in advance to reduce when actual LLM processing occurs and minimize latency impact on user experience.
2Adaptability or versatility
If LLM services are integrated for content analysis, then content generation capability is improved, but compute power requirements increase
Solution Approach 1:
The system extracts and processes only the necessary content and context locally before sending to LLM. By identifying specific content associated with the application and determining relevant context, the system minimizes the amount of data transmitted to and processed by the LLM, reducing compute power requirements while maintaining effective content generation capability.
3Adaptability or versatility
If LLM services are integrated for content analysis, then content generation capability is improved, but reliability decreases due to hallucination
Solution Approach 1:
The application serves as an intermediary between the user and the LLM. It identifies content and determines context locally, then formulates prompts that guide the LLM to provide accurate responses. This intermediary layer filters and contextualizes information before it reaches the LLM, reducing hallucination and improving reliability of generated content.
4Adaptability or versatility
If LLM services are integrated for content analysis, then content generation capability is improved, but ease of operation decreases due to complex prompt requirements
Solution Approach 1:
The system performs self-service by automatically identifying content associated with the application and determining relevant context without requiring user input. The application autonomously formulates prompts based on its understanding of the content and context, eliminating the need for users to craft complex prompts while maintaining effective content generation capability.
Data Source
AI summary
Technology is disclosed herein for the integration of large language model (LLM) services for content analysis in productivity applications. In an implementation, an application identifies content associated with the application executing on the computing apparatus. The application identifies a context relating to the content, wherein the context comprises contextual information by which to evaluate the content. The application generates a prompt for an LLM service which includes the content and the context. The prompt requests an evaluation of the content to recommend supplemental content and submits the prompt to the LLM service. The application receives a response to the prompt from the LLM service which includes a suggestion for supplemental content based on the evaluation and displays, in a user interface of the application, a recommendation based on the suggestion from the LLM service.