آموزش مهندس Full Stack هوش مصنوعی ۲۰۲۶ - هوش مصنوعی مولد و مدل‌های زبانی بزرگ (LLMs) III - آخرین آپدیت

سرفصل ها و درس ها

Introduction to Generative AI

• 1.1 What is Generative AI?

• 1.2 Evolution of Generative Models

• 1.3 Generative AI Landscape

• Hands on Lab 1

Transformer Architecture & LLM

• 2.1 Anatomy of Transformers

• 2.2 Tokens, Embeddings & Context Windows

• 2.3 How LLMs Are Trained

• Hands on Lab 2

• Clawdbot Moltbot The AI Agent

Large Language Models in Practice

• 3.1 Popular LLM Families

• 3.2 LLM Capabilities & Limitations

• 3.3 Using LLM APIs

• Hands on Lab 3

Prompt Engineering for Engineers

• 4.1 Prompt Design Fundamentals

• 4.2 Advanced Prompting Techniques

• 4.3 Prompt Robustness & Safety

• Hands on Lab 4

Embeddings & Semantic Search

• 5.1 What Are Embeddings?

• 5.2 Building Semantic Search Pipelines

• 5.3 Vector Databases

• Hands on Lab 5

Retrieval-Augmented Generation

• 6.1 Why RAG is Needed

• 6.2 RAG Architecture

• 6.3 Advanced RAG Techniques

• Hands on Lab 6

Tool Calling & Function-Based LLMs

• 7.1 Tool-Using LLMs

• 7.2 Designing Tools for LLMs

• 7.3 Multi-Step Reasoning with Tools

• Hands on Lab 7

Agentic AI Systems

• 8.1 What Are AI Agents?

• 8.2 Agent Architectures

• 8.3 Building Practical Agents

• Hands on Lab 8

Full-Stack LLM Application

• 9.1 Backend Architecture for LLM Apps

• 9.2 Frontend → LLM Integration

• 9.3 State, Memory & Context Management

• Hands on Lab 9

Evaluation, Cost & Performance

• 10.1 Evaluating LLM Outputs

• 10.2 Cost Optimization

• 10.3 Latency & Scaling Considerations

• Hands on Lab 10

Ethics, Security & Responsible AI

• 11.1 Ethical Considerations in Generative AI

• 11.2 Security Risks

• 11.3 Guardrails & Governance

How to get your Certificate

• Certificate