Generative AI Deep Linking for Accurate User Intent
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Solution Overview
Problem
Existing search systems struggle to accurately determine user intent and provide relevant information efficiently, leading to increased computational resources, network traffic, and latency.
Innovation Solution
Implementing a system that uses a language model to predict user intent based on queries and prompts, providing deep links to specific resources within the same domain, reducing the search space and generating conversational responses that align with user needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional search systems are used to provide search results, then search results can be displayed, but user intent cannot be accurately determined and relevant information cannot be provided efficiently
Solution Approach 1:
A language model is introduced as an intermediary component between the search query and the search results. The language model processes the query, predicts user intent, and generates refined search queries that are then used to retrieve results. This intermediary enables accurate intent determination while maintaining efficient information provision by filtering and prioritizing relevant results before presentation to the user.
2Reliability
If comprehensive search results are provided to ensure relevance, then search quality improves, but computational resources and network traffic increase
Solution Approach 1:
Instead of processing and displaying all possible search results, the system uses the language model to predict user intent and generates only the necessary refined queries to retrieve relevant results. This partial action approach ensures high relevance by focusing computational resources on the most promising results, thereby reducing overall computational energy consumption while maintaining reliability.
Solution Approach 2:
The language model performs preliminary processing of the user query to predict intent and generate refined search queries before actually retrieving search results. This preliminary action filters out irrelevant results early in the process, reducing the computational resources needed for result retrieval and display while ensuring that only relevant information is provided to the user.
3Loss of information
If detailed search results are displayed to improve quality, then information completeness increases, but response time and latency increase
Solution Approach 1:
The language model performs preliminary intent prediction and query refinement before result retrieval, so that when search results are fetched, they are already optimized for relevance and completeness. This preliminary action reduces the time needed to process and filter results, enabling fast response times while maintaining information completeness through targeted retrieval of the most relevant results.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium for using artificial intelligence to generate responses. In one aspect, a method includes receiving a query from a client device. Search results for resources determined to be relevant to the query are provided. The search system provides, for display with a given search result of the set of search results, a prompt input interface that enables the user to input a prompt for an artificial intelligence subsystem of the search system. A prompt input is received from the client device. An artificial intelligence subsystem uses a language model to select, from a set of resources hosted by a same domain as the corresponding resource linked to by the given search result, one or more additional resources based at least on the prompt input by the user and the query.