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)
AI
How OpenAI's AI Agent Queries 600 Petabytes
OpenAI revealed how its internal AI agent, Kepler, analyzes over 600 petabytes of data. It uses techniques like RAG and automated code analysis to overcome context limits, offering a blueprint for building large-scale AI systems.
Neeraj Dhiman ·
Data
Smarter AI Models Still Lack Context
New AI models consistently achieve higher benchmark scores, yet they often fail in real-world applications by hallucinating or mishandling queries. This gap highlights that raw intelligence isn't enough; models require specific, real-time context to perform reliably and reason effectively in production environments.
Taranpreet Singh ·
Infra
AI Retrieval Is Now Systems Problem
Scaling AI applications is revealing the limits of simple vector search. Production systems now require a complex retrieval layer that combines keyword matching, semantic search, ranking, and real-time data. This shift treats AI retrieval as a complex systems problem, not just a tooling one.
Ashish Kale ·
Infra
Why Your AI Search Needs More Than Vectors
Simple vector search is no longer enough for production AI. Companies are now building hybrid systems that combine it with ranking and personalization to deliver more relevant and useful results.
Ashish Kale ·
AI
Improving RAG with Hybrid Search
Vector search alone is often insufficient for Retrieval-Augmented Generation (RAG) systems. An analysis in InfoQ suggests a hybrid approach, combining traditional keyword search (BM25) with vector search using Reciprocal Rank Fusion (RRF), can deliver more accurate and relevant results for AI applications.
Neeraj Dhiman ·
AI
New AI coding agent runs locally
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
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.
Taranpreet Singh ·
AI
Improving AI with Graph-Enhanced RAG
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 ·
Frequently asked questions
What is retrieval-augmented generation in plain English?
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 should you use graph RAG instead of vector RAG?
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.
What's the simplest RAG stack for a small team?
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.
How do you evaluate a RAG system?
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.