AI
Retrieval-augmented generation (RAG)
What RAG is, why teams adopt it, how graph-enhanced RAG changes the architecture, and the latest releases.
Retrieval-augmented generation (RAG) grounds a large language model in your own data. Instead of relying on the model's training, the model retrieves relevant passages from a vector store at query time and conditions its answer on them. It's the dominant pattern for enterprise LLM applications where hallucination has business consequences.
Standard vector RAG works well when the answer lives in a handful of documents. It breaks down on multi-hop questions where the answer requires reasoning across structured relationships — supply chains, org charts, knowledge graphs. Graph-enhanced RAG, which Notifire has been tracking, addresses this by augmenting the vector store with explicit relationship metadata.
Latest briefings on Retrieval-augmented generation (RAG)
Data
New Postgres extension improves data handling
A new version of pg_sorted_heap, a PostgreSQL extension, has been released. It introduces physically sorted storage and integrated vector search. Version 0.14.0 adds official support for PostgreSQL 16 and is now available on the PostgreSQL Extension Network (PGXN) for easier installation and management.
Taranpreet Singh ·
Tech
Top Network Attached Storage for 2026
ZDNet has released its expert-tested review of the best Network Attached Storage (NAS) devices for 2026. The list covers top-performing hardware suitable for both home and professional environments, highlighting effortless storage solutions and including recommendations based on their own internal usage and rigorous testing.