In this article, author Aaditya Chauhan discusses the limitations of RAG pipelines based purely on vector search and how an ...
These new capabilities could help CIOs reduce the complexity of deploying AI agents into production, analysts say.
DataHub is introducing a new context intelligence layer that mines years of SQL query logs to help AI agents stop ...
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Beyond RAG as a buzzword: Aligning AI retrieval with real-world use
RAG talk is prevalent, yet simply stating “we used RAG” fails to provide much information about its effectiveness in a production environment. A more effective approach is to examine how you align ...
DataHub's Context Intelligence mines validated SQL query history to build a semantic index for AI agents. At Miro, agents hit ...
Enable semantic search on tables and columns [!INCLUDE SQL Server] Describes how to enable or disable statistical semantic indexing on selected columns that contain documents or text. Statistical ...
The standard architecture — chunking documents, embedding them into a vector database, and retrieving top-k results via ...
AI agents can’t just guess what your data means; they need an "ontology" to act as a shared rulebook so they don't make confident, expensive mistakes.
Sub-headline: BUPT researchers introduce SEA-SQL to tackle complex SQL generation via adaptive bias elimination and execution feedback. A significant technical pain point in the Text-to-SQL task is ...
Trusted Answer Search is a new offering from Oracle that prioritizes control, auditability, and predictable outcomes over generative flexibility, saving on compute but adding to data curation and ...
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