Критическая база знаний LLM за ЧАС! Это должен знать каждый.
Дмитрий Березницкий
231,875 views
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
20 Comments
Спасибо.
Где-то на 28й минуте от перегрузки сломался БАЗАметр.
Спасибо за сэкономленные месяцы жизни 👏👏👏👏👏
Критическая база знаний LLM за ЧАС! Это должен знать каждый.
Дмитрий Березницкий
231,875 views
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
Один из редчайше немногих понятных материалов для неопытных неуглубленных слушателей