AI
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.
AI
A new AI coding agent named Claw-Coder runs entirely on a local machine, addressing privacy and security concerns associated with cloud-based models. It uses Retrieval-Augmented Generation (RAG) and knowledge graphs to enhance the performance of smaller, local language models, offering a private alternative to tools like Codex.
Neeraj Dhiman ·
Data
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
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.
Ashish Kale ·
AI
Standard Retrieval-Augmented Generation (RAG) struggles with complex enterprise data like supply chains. While vector search finds similar text, it misses crucial relationships. Graph-enhanced RAG is emerging as a superior method, helping AI understand structure and answer multi-step questions more effectively.
Neeraj Dhiman ·
A pattern where an LLM looks up relevant text from your own data store before answering a question, instead of relying on its training data. The model is conditioned on the retrieved passages, which dramatically reduces hallucination on domain-specific questions.
When the answer requires multi-hop reasoning across explicit relationships — customer→account→contract, or component→supplier→country. Pure vector search finds semantically similar text but misses structural relationships. Graph RAG combines both.
A managed vector database (Pinecone, Weaviate, or pgvector on Postgres), an embedding model (OpenAI text-embedding-3 or open-source bge-large), and a retrieval-then-prompt wrapper around your existing LLM call. You can ship a working v1 in a single afternoon.
Three layers: retrieval precision/recall (did we surface the right passages?), faithfulness (does the answer match the retrieved text?), and answer quality (is the final response useful?). Tools like Ragas and TruLens automate this end-to-end.