#semantic-search

#semantic-search

A customer support rep escalated a technical issue last week that had stumped three different team members. Meanwhile, buried in a GitHub comment from a few months ago was an engineer's documentation for the exact fix. The knowledge existed, the solution was there. But connecting those dots required someone to remember a conversation that happened months ago in a completely different context.

by far the biggest use of our resources and I think the biggest opportunity right now when it comes to AI in media,

Microsoft caused a bit of a furor last year by dropping the word "deprecated" into a support document for the Control Panel. It hastily stepped back from the precipice, but the current verbiage, "the Control Panel still exists for compatibility reasons," should leave users in no doubt. Microsoft would very much like this refugee from an earlier era of Windows to be put out to pasture.

Amazon Web Services has launched S3 Vectors, a new storage solution that dramatically cuts the cost of handling vectorized data in AI by as much as 90%.

Basic RAG performed well for general topics but struggled with questions involving time, like identifying workshops next month, due to lack of metadata filtering.

Extracting metadata from research papers facilitates better academic search and retrieval dynamics. By building semantic embeddings for metadata, users gain enhanced search capabilities that match user queries with relevant content.