"You entered a few words in a text box, clicked "Search," and received a series of links. However, the results were often a mix of related, non-related, and general links. If the results didn't contain the information you needed, you reformulated your query and submitted it to the search engine again. Some of the breakdowns occurred around language-the text you matched was missing some context that disambiguated your search terms."
"With the advent of natural language models like large language models (LLMs) and foundation models (FMs), AI-powered search systems are able to incorporate more of the searcher's intelligence into the application, relieving you of some of the burden of iterating over search results. On the search side, application designers can choose to employ semantic, hybrid, multimodal, and sparse search. These methods use LLMs and other models to generate a vector representation of a piece of text"
Early web search relied on keyword matching and required users to iteratively reformulate queries to find relevant information. Language limitations and missing contextual cues produced mismatches, while user inferences generated new successful search terms. Large language models and foundation models enable AI-powered search to capture more of the searcher's intelligence and reduce the need for repeated queries. Designers can deploy semantic, hybrid, multimodal, and sparse search using vector representations for nearest-neighbor matching. Applications embed AI agents to rewrite queries, rescore results, and make multiple passes. Organizations are adding intent-based understanding and leveraging embeddings to improve context-aware results.
Read at Amazon Web Services
Unable to calculate read time
Collection
[
|
...
]