2026 AI Agent From 0 to 1 Hands-on Course: LLM + Prompts + Node Plugins + Variables JSON + Automated Workflows + RAG Knowledge Base

Course Introduction

"2026 AI Agent From 0 to 1" is a hands-on course for zero-basics users on agents and automated workflows. Starting from account registration, platform interface, and smart editing assistant configuration, it gradually covers LLM, prompts, nodes, plugins, variables, tokens, then dives into JSON, arrays, selectors, loops, batch processing, and variable aggregation. It further covers multimodal applications: TTS, AI image generation, AI video generation, text processing, visual understanding, and model/skill configuration. Through complete cases like old photo restoration, smart editing, and historical figure life, it trains automated workflow building. Finally, RAG knowledge base principles and creation/usage, helping students advance from "using AI tools" to independently building agents, automated flows, and knowledge base applications.

Who This Is For

1. Zero-basics beginners wanting to systematically learn agents
2. Creators wanting to build AI automated workflows
3. Self-media people wanting to improve content production efficiency
4. People wanting to learn AI text-image-audio-video applications
5. Professionals wanting to master RAG knowledge base building

What You'll Gain

1. Master agent building from 0 to 1
2. Learn prompts, nodes, plugins, and variable application
3. Master loops, batch processing, and JSON workflows
4. Run through AI image, video, and voice automation flows
5. Learn RAG knowledge base creation and agent application

Course Catalog:
001_1.1 Smart Editing Plugin Addition Guide
002_1.2 Smart Editing Assistant Configuration Guide
003_1.3 Smart Editing: Principle Breakdown and Practice Cases
004_2.1 Account Registration and Interface Introduction
005_2.2 Old Photo Restoration Workflow
006_3.1 LLM: The Super Brain of the Intelligent Era
007_3.2 Prompts: Instructing Agents Like a Boss
008_3.3 Nodes: Building Blocks of Automated Flows
009_3.4 Plugins: Infinite Expansion of Agent Superpowers
010_3.5 Variables: Giving Agents "Memory"
011_3.6 Tokens: Understanding Model "Word Count" and Cost
012_3.7 Credits: Efficient Use of Platform Resources
013_4.1 Variable Types: Text, Numbers, Booleans
014_4.2 JSON: Universal Language of the Intelligent World
015_4.3 JSON Arrays: Batch Processing Multiple Data Groups
016_4.4 Practice: Standardizing I/O with JSON
017_5.1 Selectors: Intelligent Branching Intersections
018_5.2 Loops: Let Agents Do Your Repetitive Work
019_5.3 Batch Processing: Tool for Large-Scale Concurrency
020_5.4 Difference Between Loops and Batch Processing
021_5.5 Variable Aggregation: Convergence of Multiple Data Streams
022_5.6 TTS: Adding Smart Narration to Videos
023_5.7 Image Generation: Custom Covers and Illustrations
024_5.8 Video Generation: Magic from Text to Video
025_5.9 Text Processing: Data Cleaning and Formatting
026_6.1 LLM Plugin Feature Introduction
027_6.2 Model Configuration: Choosing the Right "Brain" by Scenario
028_6.3 Skill Configuration: Adding Dedicated Capabilities to Models
029_6.4 Visual Understanding: Letting Agents Understand Images and Videos
030_6.5 Advanced Prompts: Optimizing LLM Output
031_7.1 Workflow Operation Tips
032_7.2 Life of a Historical Figure - Part 1
033_7.3 Life of a Historical Figure - Part 2
034_7.4 Life of a Historical Figure - Part 3
035_8.1 RAG Knowledge Base Principles
036_8.2 Creating and Using Knowledge Bases