Vector Search and RAG Tutorial – Using LLMs with Your Data

You can use Vector Search and embeddings to easily combine your data with large language models like GPT-4. I just published a course on the channel that will teach you how to implement Vector Search on three different projects. First, you will learn about the concepts and then
You can use Vector Search and embeddings to easily combine your data with large language models like GPT-4. I just published a course on the channel that will teach you how to implement Vector Search on three different projects. First, you will learn about the concepts and then I'll guide you through developing three projects. In the first project we build a semantic search feature to find movies using natural language queries. For this we use Python, machine learning

How to improve RAG results in your LLM apps: from basics to advanced, by Guodong (Troy) Zhao

3 Ways Vector Databases Take Your LLM Use Cases to the Next Level

Introduction To Retrieval Augmented Generation - Arize AI

What is Retrieval Augmented Generation (RAG) for LLMs? - Hopsworks

Making Retrieval Augmented Generation Fast

Era of Text Generation: RAG, LangChain, and Vector Databases

freeCodeCamp on LinkedIn: How to Build Your First Web Component

Running Large Language Models Privately - privateGPT and Beyond

Dev & Dsgn Frontline (@dev_dsgn) / X