Retrieval-Augmented Generation (RAG) Explained | LLMs and the Need for RAG

Описание: Retrieval-Augmented Generation (RAG) Explained | LLMs and the Need for RAG

In this video, I explain one of the most important concepts in modern AI systems — Retrieval-Augmented Generation (RAG) — and why Large Language Models (LLMs) alone are not sufficient for real-world applications.

Presented by Bindeshwar Singh Kushwaha
PostNetwork Academy

What You Will Learn

Why LLMs cannot access private or real-time data

The limitations of parametric memory

What Retrieval-Augmented Generation (RAG) is

How embeddings work

What a vector database is

Indexing pipeline vs Generation pipeline

How RAG reduces hallucination

RAG vs Fine-Tuning (clear comparison)

Local RAG setup using Ollama and FAISS

Cloud deployment of RAG systems

Freelance opportunities in RAG (2025 and beyond)

The future of AI: RAG + Tools + Agentic AI

Core Concept Explained

Instead of expecting the LLM to “remember everything,” we:

Store documents in a vector database

Convert them into embeddings

Retrieve relevant information

Inject it into the prompt

Generate grounded, factual answers

Core formula:
Answer = LLM (Question + Retrieved Context)

Technologies Discussed

LangChain

LlamaIndex

FAISS

ChromaDB

OpenAI / HuggingFace APIs

FastAPI

Streamlit / Gradio

Ollama for local LLM setup

Why This Video Matters

RAG is the backbone of modern enterprise AI systems:

Enterprise knowledge assistants

Legal AI systems

Medical document analysis

Research assistants

Codebase question answering systems

AI-powered customer support

If you master RAG today, you position yourself as a RAG and Agentic AI Developer, one of the most in-demand AI roles.

Learn More

Website: www.postnetwork.co

YouTube:    / @postnetworkacademy  

LinkedIn: www.linkedin.com/company/postnetworkacademy

GitHub: www.github.com/postnetworkacademy

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