Generative AI Search Summaries Latency Reduction
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Solution Overview
Problem
Current search engines face challenges in generating truthful and relevant responses using generative AI, often requiring extensive verified reference documents, which can lead to increased latency and resource consumption, and may not provide immediate user satisfaction with search results.
Innovation Solution
A permissions-aware search and knowledge management system that leverages generative AI to automatically generate summaries of search results by utilizing a Generative Pre-trained Transformer (GPT) model, where the system provides a set of verified search results as input prompts, includes a mechanism to limit reference documents based on latency thresholds, and generates summaries in the background to reduce latency and improve user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If extensive verified reference documents are used to generate truthful and relevant responses using generative AI, then the quality and reliability of search results is improved, but the latency and resource consumption increase
Solution Approach 1:
The system pre-generates summaries of search results in the background before users actually need them. When a search query is executed, the system checks if summaries already exist and can be displayed immediately, or if they need to be generated. This preliminary action eliminates the latency that would otherwise occur when generating summaries on-demand, while still ensuring high-quality results are provided.
Solution Approach 2:
The system separates the search result generation process into two independent components: (1) generating the actual search results from the knowledge base, and (2) generating summaries of those results. This segmentation allows the summary generation to occur independently and in advance, without blocking the delivery of search results, thereby reducing perceived latency while maintaining reliability.
2Reliability
If extensive verified reference documents are used to generate truthful and relevant responses using generative AI, then the quality and reliability of search results is improved, but the energy consumption and computing costs increase
Solution Approach 1:
Summaries are generated in advance during idle periods or low-utilization times, allowing the system to utilize computing resources more efficiently. When users submit search queries, the pre-generated summaries are already available and can be displayed immediately without requiring additional real-time computational resources, thereby reducing energy consumption during peak usage periods.
Solution Approach 2:
The system implements a caching mechanism where previously generated summaries are stored and automatically reused for identical or similar search queries. This self-service approach eliminates the need to regenerate summaries for repeated queries, significantly reducing redundant computational work and associated energy consumption while maintaining consistent high-quality results.
3Ease of operation
If search summaries are generated immediately for all user queries, then user satisfaction is improved, but the system downtime and resource availability worsen
Solution Approach 1:
The system proactively generates and caches summaries of frequently accessed or anticipated search results during periods of low system utilization. When users submit queries, the system checks its cache first and can immediately display pre-generated summaries without initiating time-consuming generation processes, thereby eliminating wait time and improving user satisfaction without causing system downtime.
Solution Approach 2:
Rather than generating summaries for every possible query, the system selectively generates summaries only for queries that are likely to be repeated or are of high importance. This partial action approach maintains user satisfaction for critical queries while avoiding the resource exhaustion and downtime that would result from attempting to pre-generate all possible summaries.
Data Source
AI summary
Methods and apparatuses for utilizing generative artificial intelligence (AI) techniques to automatically generate and display summaries of search results are described. A search and knowledge management system may generate a set of search results for a given search query and provide the set of search results (e.g., a set of verified documents that are the most relevant verified documents for the search query) as part of an input prompt to guide a generative AI model in generating a summary response of the set of search results. The generative AI model may comprise a Generative Pre-trained Transformer (GPT) model. The summary response may comprise a natural language text response and the set of search results may comprise electronic documents and messages and/or portions thereof.