Name: Retrieval-Augmented Generation (RAG) with Embeddings & Vector Databases
Brand: Scrimba
Availability: OnlineOnly
Rating: 4.9 (10 reviews)

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课程概述

In this course, you will explore advanced AI engineering concepts, focusing on the creation, use, and management of embeddings in vector databases, as well as their role in Retrieval-Augmented Generation (RAG). You will start by learning what embeddings are and how they help AI interpret and retrieve information. Through hands-on exercises, you will set up environment variables, create embeddings, and integrate them into vector databases using tools like Supabase. As you progress, you will take on challenges that involve pairing text with embeddings, managing semantic searches, and using similarity searches to query data. You will also apply RAG techniques to enhance AI models, dynamically retrieving relevant information to improve chatbot responses. By implementing these strategies, you will develop more accurate, context-aware conversational AI systems. This course balances both the theory behind AI embeddings and RAG with practical, real-world applications. By the end, you will have built a proof of concept for an AI chatbot using RAG, preparing you for more advanced AI engineering tasks....

1 - Retrieval-Augmented Generation (RAG) with Embeddings & Vector Databases 的 10 个评论（共 10 个）

创建者 Syed A H

•

Nov 18, 2024

This is an exceptional course taught by Guil, Covers most of the basics and lays a solid foundation on vector databases and embeddings concept with easy to understand examples combining concepts learnt from the previous courses in this specialization was fun. I would recommend it to everyone learning to build LLM applications in javascript.

创建者 Vincent K

•

Mar 22, 2024

las apps deben ser casos de uso utiles, mas que recomendar peliculas. Y si no tengo permitido salir a internet, como funciona localmente todo lo aprendido?