According to @DeepLearningAI, researchers from Google and Johns Hopkins show that single-embedding retrievers cannot, in principle, retrieve all relevant document combinations as databases scale, identifying theoretical limits tied to embedding size that set realistic expectations for retrieval augmented generation systems, source: @DeepLearningAI. For traders, these limits are directly relevant when assessing AI infrastructure and data indexing narratives in crypto that depend on RAG and vector databases, source: @DeepLearningAI.

According to DeepLearning.AI, Qdrant is sponsoring AI Dev 25, highlighting their contribution to advancing open-source vector search technology. Attendees are encouraged to visit their demo table to see their vector database in action and engage with the team at SHACK15 on Friday.