Learn RAG from Scratch - RAG Chunking Strategies [Top 11] | Semantic Chunking to LLM Chunking | 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

Summary

Keywords

RAG Chunking RAG LLM Retrieval Augmented Generation LLM Chunking Strategies Python LangChain Context Length LLM Chunking Techniques AI Prompt Engineering Fixed Size Chunking Sentence Based Chunking Semantic Chunking Token Based Chunking

Full Transcript

Welcome to the FreeBirds Crew! 🚀 In this second video of our Learn RAG from Scratch Playlist, we dive deep into chunking strategies for Retrieval-Augmented Generation (RAG). Learn: ✅ How chunking impacts LLM performance ✅ The trade-offs between long and short contexts in LLMs ✅ Step-by-step implementation of chunking using Python and LangChain ✅ 11 advanced chunking techniques explained with examples Topics Covered: 1. Understanding context length of LLM and its impact on LLM responses 2. How to calculate tokens in any text 3. Chunking strategies: fixed size, sentence-based, semantic, token-based, and more! Whether you're building advanced LLM applications or optimising your RAG workflows, this video is packed with practical insights and code examples to get you started. 💻 Code Walkthrough: Python examples to implement chunking for different data types. 👉 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,