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Build a RAG Chatbot From Scratch | Upload PDFs and Ask Questions (Step-by-Step Guide)
🤖 Welcome to KishoreLytics!
In this video, we’ll learn and build one of the most powerful concepts in modern AI — **RAG (Retrieval-Augmented Generation)**.
We’ll break down the **architecture of RAG**, understand how it enhances LLMs like ChatGPT, and finally **build a real RAG Chatbot** where users can upload their own PDFs and ask questions — all step by step!
---
✅ What You’ll Learn:
- What is RAG and why it’s a breakthrough in AI
- RAG architecture explained (Retriever + Generator)
- How RAG connects your data to a language model
- How to create embeddings and store vector data
- Building a chatbot that answers questions from uploaded PDFs
- Using LangChain and vector databases in real time
- Full workflow: Upload → Process → Query → Respond
---
🎯 Who Should Watch:
- Beginners learning AI and Large Language Models
- Developers & Data Scientists building chatbots
- Students exploring NLP, Generative AI, and LangChain
- Anyone who wants to build a real-world AI project from scratch
---
💡 After watching this video, you’ll understand **how RAG systems work**, and you’ll build your own **AI chatbot** that can interact with your custom documents.
📺 Subscribe to @KishoreLytics for complete tutorials on RAG, LangChain, Python, SQL, and real AI projects — all 100% free!
💬 Drop your RAG project doubts in the comments — I reply to everyone.
---
🔗 Useful Links:
📘 Notes & Code Repository: https://github.com/Kishorelytics/Sql-Course
🌐 Portfolio: https://kishorelytics.netlify.app
💼 LinkedIn: https://www.linkedin.com/in/kishore-kannan-n-/
🎓 Try Free Practice Projects: https://lms-kishorelytics.vercel.app
---
🔖 Hashtags:
#RAGTutorial #RAGArchitecture #RAGChatbot #RetrievalAugmentedGeneration #LangChain #AIChatbot #BuildWithKishoreLytics #LearnAI #GenerativeAI #ChatGPTProjects #PythonAI #MachineLearning #VectorDatabase #AI2025 #LLMTutorial #DataScience #OpenAI #KishoreLytics
Видео Build a RAG Chatbot From Scratch | Upload PDFs and Ask Questions (Step-by-Step Guide) канала Kishorelytics
In this video, we’ll learn and build one of the most powerful concepts in modern AI — **RAG (Retrieval-Augmented Generation)**.
We’ll break down the **architecture of RAG**, understand how it enhances LLMs like ChatGPT, and finally **build a real RAG Chatbot** where users can upload their own PDFs and ask questions — all step by step!
---
✅ What You’ll Learn:
- What is RAG and why it’s a breakthrough in AI
- RAG architecture explained (Retriever + Generator)
- How RAG connects your data to a language model
- How to create embeddings and store vector data
- Building a chatbot that answers questions from uploaded PDFs
- Using LangChain and vector databases in real time
- Full workflow: Upload → Process → Query → Respond
---
🎯 Who Should Watch:
- Beginners learning AI and Large Language Models
- Developers & Data Scientists building chatbots
- Students exploring NLP, Generative AI, and LangChain
- Anyone who wants to build a real-world AI project from scratch
---
💡 After watching this video, you’ll understand **how RAG systems work**, and you’ll build your own **AI chatbot** that can interact with your custom documents.
📺 Subscribe to @KishoreLytics for complete tutorials on RAG, LangChain, Python, SQL, and real AI projects — all 100% free!
💬 Drop your RAG project doubts in the comments — I reply to everyone.
---
🔗 Useful Links:
📘 Notes & Code Repository: https://github.com/Kishorelytics/Sql-Course
🌐 Portfolio: https://kishorelytics.netlify.app
💼 LinkedIn: https://www.linkedin.com/in/kishore-kannan-n-/
🎓 Try Free Practice Projects: https://lms-kishorelytics.vercel.app
---
🔖 Hashtags:
#RAGTutorial #RAGArchitecture #RAGChatbot #RetrievalAugmentedGeneration #LangChain #AIChatbot #BuildWithKishoreLytics #LearnAI #GenerativeAI #ChatGPTProjects #PythonAI #MachineLearning #VectorDatabase #AI2025 #LLMTutorial #DataScience #OpenAI #KishoreLytics
Видео Build a RAG Chatbot From Scratch | Upload PDFs and Ask Questions (Step-by-Step Guide) канала Kishorelytics
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9 декабря 2025 г. 19:30:53
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