Learn RAG from Scratch - ⁣What is Retrieval-Augmented Generation (RAG)? | Why LLMs Hallucinate? | Learn RAG from the Scratch

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

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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

Summary

Keywords

rag framework langchain rag tutorial python rag application vector database for llm ai chatbot with rag rag architecture explained llm rag integration retrieval augmented generation freebirds crew rag playlist tutorial rag with python rag llm hands-on

Full Transcript

Welcome to the FreeBirds Crew! 🚀 In this first video of our Learn RAG from Scratch Playlist, we dive into why Large Language Models (LLMs) sometimes hallucinate and how the Retrieval-Augmented Generation (RAG) framework can solve this issue. Large language models usually give great answers, but because they're limited to the training data used to create the model. Over time, they can become incomplete—or worse, generate answers that are just plain wrong. One way of improving the LLM results is called "retrieval-augmented generation," or RAG. Simranjeet Singh explains the LLM/RAG framework and how this combination delivers two big advantages, namely: the model gets the most up-to-date and trustworthy facts, and you can see where the model got its info, lending more credibility to what it generates. 🔍 Topics Covered: 1. Why LLMs hallucinate and provide incorrect information. 2. What is Retrieval-Augmented Generation (RAG)? 3. How RAG architecture works with LLMs to enhance accuracy. 3. A hands-on demo creating a RAG + LLM application using Python and LangChain on BBC News Dataset. 👉 Don’t forget to like, subscribe, and hit the bell icon for more AI tutorials and project walkthroughs. 📂 Playlist: Learn RAG from Scratch Join this channel to get access to perks: https://www.youtube.com/channel/UC4RZP6hNT5gMlWCm0NDzUWg/join 🚀 Follow me on Medium for the latest blogs and projects: https://bit.ly/3JGXqwc 🗂️ To get the Source Code, Follow me on GitHub: https://bit.ly/3gg07Uc 🔖 Book your call with me at topmate.io and learn how to harness the latest technologies power and speed up your learning process. 📲 Book your call at https://bit.ly/43TLDCD Playlists that make you skilled up 📍 Prompt Engineering: https://bit.ly/42v376M 📍 Finacial Data Analysis and Financial Modelling: https://bit.ly/3OCWI5O 📍 Artificial Intelligence Projects: https://bit.ly/3L8lhEi 📍 Predict IPL 2023 Winner: https://bit.ly/3BfC3N9 📍 Machine Learning: https://bit.ly/3gsuIxb 📍 Face Recognition: https://bit.ly/2YphpHm 📍 Creative Python: https://bit.ly/34nM9wr 📍 Latest Tech Videos: https://bit.ly/2QcaOeW 📱Follow US on Social media. Telegram: https://bit.ly/3JJblSC Youtube: https://bit.ly/38gLfTo Instagram: http://bit.ly/2N1IMP9 - Twitter: https://bit.ly/40vYMjl ⚡️ Do like, comment, share, and subscribe to our YouTube channel for more videos and projects. Krish Naik genai, Krish Naik llm, Krish naik rag, Krish Naik Python tutorial, Krish naik explainable ai, Krish Naik data science, Krish Naik statistics, machine learning full course, machine learning tutorial, machine learning interview questions, machine learning projects in Python, data science for beginners, data science project, data science full course, data science interview questions, data science interview, machine learning interview questions, statistics interview questions, Python interview questions, interview questions, leetcode questions, interview preparation for FAANG, FAANG Interview questions, Google data science Interview questions, Amazon data scientist interview, Meta data scientist interview questions,