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 ...
Security Boulevard

Building a RAG system on Databricks with your unstructured data using Tonic Textual

Databricks and Tonic.ai have partnered to simplify the process of connecting enterprise unstructured data to AI systems to reap the benefits of RAG. Learn how in this step-by-step technical how-to.
Geeky Gadgets

How to build a Retrieval-Augmented Generation (RAG) system

Retrieval-Augmented Generation (RAG) systems have emerged as a powerful approach to significantly enhance the capabilities of language models. By seamlessly integrating document retrieval with text ...
SiliconANGLE

Contextual AI nabs $80M for its ‘RAG 2.0’ platform

Contextual AI Inc., a startup that helps enterprises build retrieval-augmented generation artificial intelligence applications, has closed a $80 million Series A round to support its commercialization ...
InfoWorld

How to build RAG at scale

Retrieval-augmented generation breaks at scale because organizations treat it like an LLM feature rather than a platform discipline. Enterprises that succeed with RAG rely on a layered architecture.
InfoWorld

Surveying the LLM application framework landscape

Large language models by themselves are less than meets the eye; the moniker “stochastic parrots” isn’t wrong. Connect LLMs to specific data for retrieval-augmented generation (RAG) and you get a more ...
VentureBeat

Exclusive: VectorShift raises $3M to modularize LLM application development

Today, VectorShift, a startup working to simplify large language model (LLM) application development with a modular no-code approach, announced it has raised $3 million in seed funding from 1984 ...