In this session, we will explore how hybrid search can boost the abilities of AI and LLMs. Traditional search methods often fall short, either by failing to return any results or providing irrelevant ones. By applying hybrid search methods — which combine keyword search algorithms with cutting-edge vector search algorithms such as k-NN and HNSW — we can greatly improve the relevance of results in AI-powered systems.This approach has concrete applications that can make a noticeable difference in various fields. It helps in fine-tuning video search capabilities for gamers looking through extensive video game content. It refines recommendation engines for services like Netflix and Amazon, allowing for better-curated content delivery. Moreover, it can strengthen fraud detection systems, making them more adept at identifying suspicious activities by sifting through complex data patterns.
Throughout the discussion, we will consider the challenges of integrating these hybrid search techniques into existing LLM systems, focusing on maintaining a balance between swift performance and high relevancy in real-world settings. Join us to learn about the advanced search strategies that are setting new standards for AI and LLM applications.
