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Master RAG & LLMs: Build End-to-End GenAI Projects

Master RAG & Build 9 Real-World GenAI Projects from Scratch

4.0 (7)

140 learners

What you'll learn

Explain the core concepts of RAG and why it mitigates LLM hallucinations.
Implement text chunking and create vector embeddings for a RAG pipeline.
Build a functional RAG system using LangChain, a vector database, and an LLM.
Deploy a RAG application with a user interface for document-based question answering.

This course includes

2.5 hours of video
Certificate of completion
Access on mobile and TV

Course content

1 modules • 9 lessons • 2.5 hours of video

Master RAG & LLMs: Build End-to-End GenAI Projects

9 lessons • 2.5 hours

▶

⁣What is Retrieval-Augmented Generation (RAG)? | Why LLMs Hallucinate? | Learn RAG from the Scratch10:06
RAG Chunking Strategies [Top 11] | Semantic Chunking to LLM Chunking | Learn RAG from Scratch26:11
GenAI Vector Embeddings Explained: Create, Store, Search | ChromaDB & FAISS | Learn RAG from Scratch17:00
GenAI RAG LLM Retrieval Strategies and Algorithms | Ranking Algorithms | Learn RAG from Scratch23:23
GenAI RAG Teaching Assistant with LangChain and ChromaDB | Multiple LLMs QWEN - PHI2 - Mistral14:35
GenAI QNA Chatbot with Blog URL and PDF Using LangChain RAG and Google Gemini Pro LLM | Streamlit UI12:22
DeepSeek R1 vs Google Gemini [Comparison] Ollama FAISS VectorDB RAG Streamlit GenAI Project Tutorial15:44
Will KAG surpassed RAG? KAG [Knowledge Augmented Generation] Architecture and Working Explained17:28
Build 9 End-to-End GenAI Projects before 2025 [ RAG + Agentic AI + LLMs ] FAANG Level System Design18:47

RAG

LLM hallucinations

text chunking

vector embeddings

LangChain

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