How to Build a Scalable RAG System for AI Apps (Full Architecture)

Sponsored by Databricks Neon → https://get.neon.com/bl6FZuG

Large language models do not know your private company data. They are trained on public information, not your internal documents. That is where RAG (Retrieval Augmented Generation) comes in.

In this video, I break down how production-ready RAG systems are actually built, beyond simple demos and tutorials. You will learn how real companies design RAG systems that scale, stay accurate, and work reliably in production.

We also look at how serverless Postgres with vector support (like Neon) fits into modern AI architectures.

Resources:

- RAG Basics: https://youtu.be/MlhZPTfOJBo?si=iShRl5LOPq3cbdGk
- Databricks Neon: https://get.neon.com/bl6FZuG
- System Design Course: https://academy.bytemonk.io/courses
- ByteMonk Blog: https://blog.bytemonk.io/
- LinkedIn: https://www.linkedin.com/in/bytemonk/
- Github: https://github.com/bytemonk-academy
- AWS Summary: https://aws.amazon.com/message/101925/

Timestamps
00:00 LLMs Don’t Know Your Private Data
00:37 What Is RAG? (Simple Explanation)
01:45 Why Bad Retrieval Causes Hallucinations
03:35 Demo RAG vs Real Production Systems
04:45 Production RAG Architecture (Full Overview)
06:00 Data Ingestion & Smart Chunking
07:50 Metadata, Summaries & Question Generation
09:05 Why You Need Vector + Relational Databases
10:15 Using PostgreSQL + pgVector in Production
11:15 Hybrid Search (Semantic + Keyword)
12:00 Reasoning Engine & Multi-Agent Systems
13:15 Validation Layer (Stop Hallucinations)
14:05 Evaluation Metrics (Precision, Recall, Cost)
14:55 Red Teaming & Stress Testing





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AWS Certification:
AWS Certified Cloud Practioner: https://youtu.be/wF1pldkQrOY
AWS Certified Solution Architect Associate: https://youtu.be/GzomXNLFgkk
AWS Certified Solution Architect Professional: https://youtu.be/KFZrBxSA9tI

#RAG #LLM #AIArchitecture #systemdesign