Критическая база знаний LLM за ЧАС! Это должен знать каждый.

LLM Concepts for Developers: From Tokens to AI Agents in Production A complete guide to the fundamental concepts of working with language models for practicing engineers. In this video, we explore LLM architecture, the mechanics of Transformers, context management, API cost optimization, and building production-ready AI systems. You'll learn the difference between prompt engineering and context engineering, understand when to use RAG and when to fine-tune, and learn how to build a conscious architecture for AI solutions. The video covers: How tokens, attention mechanisms, and transformers work—the architecture behind GPT, Claude, and other LLMs Why context windows are critical for AI assistants and how to manage them in Cursor, Claude Code, and other tools The difference between prefill and decode phases, cost optimization through caching, and proper API usage LLMs vs. Reasoning models vs. AI agents—three levels of complexity and when to use each Context engineering: why context is more important than prompts and how to structure information for agents RAG, in-context learning, and fine-tuning—three ways to give AI missing knowledge and when to use each Why this video is important for you: Most developers use AI tools blindly, without understanding the basic concepts. This leads to unpredictable results, bloated API budgets, and systems that work "sometimes" rather than reliably. Understanding the fundamental principles is the difference between "working sometimes" and "working in production." Understand the architecture of modern AI systems and start using LLM consciously today. 0:00 - Introduction: Why Understanding Fundamental AI Concepts Is Important 1:49 - How LLM Works: Tokens, Attention, and Transformers 7:02 - Context Window: Model Working Memory and Its Limitations 10:18 - Prefill and Decode: The Mechanics of Response Generation 13:38 - Caching: How to Reduce Costs by 70-90% 16:20 - Training vs. Inference and Creativity Control 21:39 - LLM, Reasoning Models, and AI Agents: Three Levels of Complexity 27:25 - Context Engineering: Why Context is More Important than Prompts 35:47 - Three Ways to Give AI Knowledge: In-Context, RAG, and Fine-Tuning 43:36 - API vs. Self-Hosted Models and Practical Examples 45:46 - Foundation Models, MCP, and Mixture of Experts 52:46 - AI Security: Threats and System Protection 55:05 - Conclusion