Explore >> Select a destination

You are here

 www.tensorops.ai
Vector DBs Will Not Save Your RAG
| | bdtechtalks.com
No-code retrieval augmented generation (RAG) with LlamaIndex and ChatGPT - TechTalks
11.3 parsecs away

Travel
| | Retrieval augmented generation (RAG) enables you to use custom documents with LLMs to improve their precision.
| | jannikreinhard.com
Deep Dive into Co-Pilots: Understanding Architecture, LLMs, and Advanced Concepts - AI & Modern Device Management
10.2 parsecs away

Travel
| | In this blog post I want to deep dive with you how LLMs and CoPilots work, want to give an explanations into the most important aspects and show you some important architecture aspects and concepts. We will not build an own Copilot but I will share also some reference architectures and a tool I created...
| | www.ai21.com
The Promise of RAG: Bringing Enterprise Generative AI to Life
11.3 parsecs away

Travel
| | By grounding AI in an organization's unique expertise, Retrieval-Augmented Generation (RAG) helps enterprises overcome hurdles in deploying large language models. AI21's RAG Engine provides advanced retrieval capabilities without enterprises having to invest heavily in development and maintenance.
| | unstructured.io
Choosing the right embedding model for your RAG application: a comprehensive guide - Unstructured
87.4 parsecs away

Travel
| Navigate the Massive Text Embedding Benchmark (MTEB) leaderboard with confidence! Understand the difference between Bi-Encoders and Cross-Encoders, learn how text embedding models are pre-trained and benchmarked, and how to make the best choice for your specific use case.