MIT's MeMo lets teams swap in a better LLM without retraining — and performance jumps 26%

MIT's MeMo keeps AI memory separate from reasoning, so teams can upgrade their LLM without retraining and see a 26% performance gain, researchers say.
InfoWorld

Retrieval-augmented generation, step by step

RAG is a pragmatic and effective approach to using large language models in the enterprise. Learn how it works, why we need it, and how to implement it with OpenAI and LangChain. Typically, the use of ...
Benzinga.com

Dnotitia Secures 16 Billion KRW Government Projects to Advance Vector Database Technology for LLM

Dnotitia, an innovative AI startup, has won two major South Korean government projects worth 16 billion KRW. SEOUL, SOUTH KOREA, September 10, 2024 /EINPresswire.com ...
techtimes

How RAG and Vector Databases Improve LLMs

Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs) are two distinct yet complementary AI technologies. Understanding the differences between them is crucial for leveraging their ...
MUO on MSN

Local LLM setup: how to use RAG and an embedding model to stop wasting context

Local LLMs degrade fast when context fills up. An embedding model and RAG pipeline fixes that — and runs entirely on your ...
dbta

Augmenting Applications with Vector Search and RAG

AI is undoubtedly a formidable capability that poses to bring any enterprise application to the next level. Offering significant benefits for both the consumer and the developer alike, technologies ...
Geeky Gadgets

How to build Large Language Models (LLM) and RAG pipelines using open-source models

In the world of artificial intelligence, the ability to build Large Language Model (LLM) and Retrieval Augmented Generation (RAG) pipelines using open-source models is a skill that is increasingly in ...