Summary
Full Transcript
In this lesson, you’ll learn the core building blocks of Generative AI—the concepts that show up again and again in real-world GenAI systems and in AWS certification exam questions. We start with tokenization and chunking (how text becomes tokens and how long documents are split into manageable pieces), then move into the context window (how many tokens a model can “remember” at once). From there, we break down embeddings and vectors (how meaning is represented numerically), how those vectors are stored in vector databases (for fast similarity search), and why transformers power modern LLMs like GPT, BERT, and T5. Finally, we cover foundation models, multimodal AI, and diffusion models used for high-quality image generation. What you’ll learn Tokenization vs. chunking (and why both matter) Common tokenization types: word, subword/BPE, character Chunking strategies: fixed-length, sentence-based, semantic Context window limits and tradeoffs (cost vs. coherence) Embeddings → vectors: how meaning becomes numbers Why vector databases power semantic search + RAG Transformer basics: self-attention, multi-head attention, positional encoding Foundation models and how they enable reusable AI capabilities Multimodal models (text + image + audio) and where they’re used Diffusion models for text-to-image generation Who this video is for AWS learners and certification candidates Developers and architects building GenAI apps Anyone learning RAG, LLMs, and GenAI fundamentals Hands-on practice links Try tokenization here: https://platform.openai.com/tokenizer #GenerativeAI #Tokenization #Chunking #ContextWindow #Embeddings #VectorDatabase #Transformers #LLM #RAG #AWS #AmazonBedrock #MachineLearning #DeepLearning #AIExplained #DiffusionModels
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