Retrieval-Augmented Generation Is An Engineering Problem, Not A Model Problem

In practice, retrieval is a system with its own failure modes, its own latency budget and its own quality requirements.
Morningstar

Zilliz Open Sources Industry-First Bilingual "Semantic Highlighting" Model to Slash RAG Token Costs and Boost Accuracy

REDWOOD CITY, Calif., Jan. 30, 2026 /PRNewswire/ -- Zilliz, the company behind the leading open-source vector database Milvus, today announced the open-source release of its Bilingual Semantic ...
The Japan Times

Kotozna Launches "TocDex RAG" to Enhance Accuracy and Multilingual Capabilities in Generative AI

Kotozna Inc. (Head Office: Minato-ku, Tokyo; CEO: Genri Goto), a B2B SaaS company specializing in generative AI–powered ...
Analytics Insight

Best Vector Database Options for RAG in 2026 Explained

Overview RAG is transforming AI apps, and vector databases are the engine behind accurate, real-time responsesChoosing the ...

Databricks research shows multi-step agents consistently outperform single-turn RAG when answers span databases and documents

Data teams building AI agents keep running into the same failure mode. Questions that require joining structured data with ...
Semiconductor Engineering

Rethinking The Role Of CPUs In AI: A Practical RAG Implementation

In many enterprise environments, engineers and technical staff need to find information quickly. They search internal documents such as hardware specifications, project manuals, and technical notes.
VentureBeat

MemRL outperforms RAG on complex agent benchmarks without fine-tuning

A new technique developed by researchers at Shanghai Jiao Tong University and other institutions enables large language model agents to learn new skills without the need for expensive fine-tuning. The ...