Buzz words of AI Explained

Before you can build an agent harness, you need to understand what’s actually inside the model and the system around it. This video covers every core concept in one place, from tokens to agents. Apologizing for my bad english fluency ! What we cover: Model Foundations: LLMs, tokenization, vectorization, self-supervised learning, transformers Development & Optimization: fine-tuning, RAG, vector databases, Model Context Protocol (MCP), context engineering Advanced Capabilities: agents, reinforcement learning, chain of thought & reasoning models, multi-modal models, small language models & distillation, quantization Basic Defination : Gen AI, Agentic AI, Deep Agents, Workflows This is the groundwork for my harness engineering series. Whether you’re a beginner or just need a refresher, these are the building blocks behind every AI agent. Notion Link : https://app.notion.com/p/vishalk988/E... Timestamps : 0:00 Intro 1:16 Bird Eye 4:48 Tokenization 9:21 Vectors 17:20 Self-Supervized Learning 22:50 Transformers 34:15 Fine Tuning 38:48 RAG 47:40 Vector DB 48:50 MCP & TOOLS 51:46 GEN AI, AGENTS, AGENTIC AI , DEEP AGENTS, WORKFLOWS 55:46 Context Engineering 1:01:17 Reinforcement Learning 1:04:55 Chain Of Thought Termination 1:11:10 Reasoning Model 1:12:33 Small Language Model 1:13:34 Distillation, Pruning, Quantization 1:16:58 Context Rot Happens #GenAI #LLM #RAG #MCP #AIAgents #HarnessEngineering #ContextEngineering