Learn Generative AI Foundations in 3 Hours | 60+ Topics Covered

In this complete AI Foundations video, we will learn the core concepts behind Generative AI in one structured session. This video is designed for beginners, cloud engineers, DevOps engineers, developers, architects, and anyone who wants to understand Generative AI properly from the ground up. We will cover 60+ important topics across the full Generative AI landscape, including foundation models, LLMs, SLMs, tokens, embeddings, model training, transformers, prompt engineering, RAG, multimodal AI, AI agents, enterprise readiness, and the future direction of AI. You will learn: The Big Picture: Generative AI vs Traditional AI, Predictive AI vs Generative AI, Foundation Models, Large Language Models (LLMs), Small Language Models (SLMs), Model Openness, and Model Selection. How Information Enters an AI System: Tokens and tokenisation, Embeddings, Vector representations, and Semantic similarity. How Models Are Built and Trained: Pretraining, Instruction tuning, Alignment (RLHF, DPO), Fine-tuning, LoRA, QLoRA and PEFT, Quantisation, Model distillation, and Synthetic data. Transformers & Inference: Transformer architecture, Attention, Encoder, decoder and decoder-only models, Context window, Inference, KV cache, and Speculative decoding. Prompt Engineering: Prompt engineering, System prompts, Prompt templates, Zero-shot prompting, One-shot prompting, Few-shot prompting, Chain of Thought, ReAct, Reflexion, Structured output, Function calling, Tool calling, Grounding, Hallucination, and Knowledge cutoff. Search, Knowledge and RAG: Vector search, Retrieval-Augmented Generation (RAG), Chunking, Metadata, Vector databases, The RAG pipeline, RAG evaluation, Fine-tuning vs RAG, GraphRAG, and Agentic RAG. Beyond Text: Multimodal AI: Multimodal AI, Vision Language Models (VLMs), Image generation, Video generation, Voice AI, and Real-Time AI. AI Agents and Agentic Systems: What an AI agent is, AI agent vs chatbot, Memory, Planning, Tool use, Multi-agent systems, MCP, A2A communication, and LLM application frameworks. Production and Enterprise Readiness: Model routing, Model deployment, Model hosting, AI application architecture, LLMOps, GenAIOps, Evaluation, Benchmarking, Security, Responsible AI, and Governance. The Future Direction of Generative AI: Reasoning models, Long context models, Mixture of Experts (MoE), Remote coding agents, Computer use, and the future direction of AI. Timeline: 00:00 Introduction 00:26 The Big Picture of Generative AI 20:33 How Information Enters an AI System 33:38 How Models Are Built and Trained 51:48 Transformers & Inference 1:13:00 Prompt Engineering 1:31:00 Search, Knowledge and RAG 2:03:00 Multimodal AI 2:20:00 AI Agents and Agentic Systems 2:38:00 Production and Enterprise Readiness 3:03:00 The Future Direction of Generative AI By the end of this video, you will have a strong foundation to understand how modern AI systems work and how all the major Generative AI concepts connect together.