Tokenization & Embeddings Explained in Hindi | How LLMs Understand Text 🤖

🤔 *How does ChatGPT convert your words into something an AI model can process?* Ever wondered how AI works with text? In this video, we explain Tokenization and Embeddings using simple examples in Hinglish. You'll learn: ✅ What is Tokenization in LLMs? ✅ Word, character and subword tokenization ✅ How Byte Pair Encoding (BPE) works ✅ What are Embeddings and Vector Representations? ✅ How semantic similarity and Cosine Similarity work ✅ Why token count matters for LLM context windows and API costs Whether you're a BTech student, software engineer, developer, or AI enthusiast, this video will help you understand two important building blocks behind modern Large Language Models. 📌 *In simple words:* Tokenization breaks text into tokens, while embeddings represent those tokens as numerical vectors that models can process. Subscribe to *Nextgen Engineer* for practical explanations of AI, LLMs, RAG, MCP, cloud computing, and software engineering concepts. #Tokenization #Embeddings #LLM #GenerativeAI #ArtificialIntelligence