Django Generative AI RAG Project | Working With

Описание: #django #generativeai #llm #projects

Join this channel to get access to the perks:
   / @saikumarreddyn  

Welcome to Episode 10 of the Django GenAI series!

In this practical tutorial, you'll dive deep into Retrieval-Augmented Generation (RAG) within Django. Learn exactly how RAG functions, its key characteristics, and how it improves Generative AI projects.

In this session, you’ll clearly understand:

✅ What is RAG and how does it work? (Simplified explanation) ✅ Key characteristics of RAG you should know ✅ Step-by-step setup guide for Quadrant VectorDB in Django ✅ Hands-on project discussion with best practices

Who should watch?

Django & Python developers interested in Generative AI

AI/ML engineers and enthusiasts

Anyone aiming to integrate VectorDBs into their Django applications

Learners looking to build powerful AI-driven web applications

🔗 Don't forget to subscribe & Stay Tuned for upcoming videos with more Django + Generative AI insights!

link - https://github.com/ApexIQ/GenAI_with_...

🔔 Subscribe and hit the bell icon so you won’t miss upcoming tutorials!

📢 Comment below if you're excited to start building with Generative AI and Django!

#GenerativeAI #Django #LLM #OracleCloud #AIProjects #RAG #PythonDevelopment #WebDevelopment #Bootcamp

Generative AI
Django Bootcamp
Large Language Models
LLM Django Tutorial
RAG in AI
Oracle Cloud Free Tier
Production Ready AI
Generative AI with Django
AI Project Deployment
Django AI Integration
Django Web Development
AI Python Projects
Django RAG integration
Quadrant VectorDB Django
Retrieval-Augmented Generation
RAG in Django
VectorDB setup tutorial
Django AI tutorial
Generative AI Django
RAG explained simply
Django GenAI series
AI web applications
AI project setup Django
VectorDB practical guide
Django Machine Learning
AI database integration
Python AI development
RAG Project
Project