Generative AI Search Aggregation for Faster Semantic Content Retrieval
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Solution Overview
Problem
Existing content searching systems are slow and expensive in live production, and traditional methods struggle with matching literal query text to media results, leading to poor performance and lack of transparency in AI decision-making.
Innovation Solution
Utilize generative AI models to confabulate search results and leverage embeddings of query-representative items, combining them with traditional search systems for faster and more relevant content retrieval, while ensuring transparency through human-intelligible explanations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional search systems are used for content retrieval, then processing speed is fast, but relevance and semantic matching performance are poor
Solution Approach 1:
The system pre-generates confabulated items and their embeddings before actual search queries are submitted. This preliminary action creates a ready-to-use representation of ideal search results, enabling fast retrieval without sacrificing semantic matching quality when queries are received.
Solution Approach 2:
The system introduces confabulated items as an intermediary between the query and actual database items. These confabulated items serve as mediators that capture semantic meaning and enable accurate matching while allowing traditional fast search algorithms to operate on the pre-processed representations.
2Measurement precision
If generative AI models are used to generate search results, then semantic matching and relevance improve, but processing speed decreases and costs increase
Solution Approach 1:
The system performs the computationally intensive generative AI processing in advance to create confabulated items and embeddings. By moving the heavy computation to a preliminary stage, the actual search operation can quickly retrieve and match against pre-generated representations without experiencing the speed penalty of real-time generation.
3Measurement precision
If generative AI models are used to generate search results, then semantic matching and relevance improve, but processing costs increase
Solution Approach 1:
The system performs expensive generative AI processing once in advance to create reusable confabulated items and embeddings. This preliminary action amortizes the high computational cost over multiple subsequent search queries, making the overall system cost-effective despite the expensive initial generation phase.
Solution Approach 2:
The system creates confabulated items that are representations or copies of ideal search results. These copied representations can be reused multiple times without incurring additional generation costs, reducing the per-query cost while maintaining high relevance through the quality of the initial generative AI processing.
4Ease of operation
If traditional search methods are used, then processing is fast and cheap, but transparency and explainability of AI decisions are lacking
Solution Approach 1:
The system uses confabulated items as intermediaries that make AI decision-making transparent. These confabulated items represent the reasoning process of the generative AI model in a human-understandable format, allowing users to see why certain results were selected while enabling fast traditional search algorithms to operate on the structured representations.
Data Source
AI summary
Methods and systems provide content searching and retrieval using generative artificial intelligence (AI) Models. The system is configured to receive a user search for content, media or item listings. The user search is provided to a generative AI based search sub-system and to a traditional search sub-system. A first search result listing is generated by the generative AI based subsystem, and a second search result listing is generated by the traditional search sub-system. The first search result listing and the second search result listing are aggregated together and provided for display to a user client device.