Learn RAG from Scratch
Master RAG & Build 9 Real-World GenAI Projects from Scratch
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19 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
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 Scratch 10:07
- RAG Chunking Strategies [Top 11] | Semantic Chunking to LLM Chunking | Learn RAG from Scratch 26:11
- GenAI Vector Embeddings Explained: Create, Store, Search | ChromaDB & FAISS | Learn RAG from Scratch 17:01
- GenAI RAG LLM Retrieval Strategies and Algorithms | Ranking Algorithms | Learn RAG from Scratch 23:23
- GenAI RAG Teaching Assistant with LangChain and ChromaDB | Multiple LLMs QWEN - PHI2 - Mistral 14:35
- GenAI QNA Chatbot with Blog URL and PDF Using LangChain RAG and Google Gemini Pro LLM | Streamlit UI 12:22
- DeepSeek R1 vs Google Gemini [Comparison] Ollama FAISS VectorDB RAG Streamlit GenAI Project Tutorial 15:44
- Will KAG surpassed RAG? KAG [Knowledge Augmented Generation] Architecture and Working Explained 17:29
- Build 9 End-to-End GenAI Projects before 2025 [ RAG + Agentic AI + LLMs ] Scalable System Design 18:47
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