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AI Learning Hub

Start with the path that fits you

You do not need to understand prompting, RAG, and agents first. Is repetitive work taking over, is a product idea still unbuilt, are you moving into AI engineering, or does every content piece start from a blank page? Pick the closest one and solve that problem first.

No long catalogue to decodeTry it for one hour firstLeave with something useful
Start with work already on your desk
Make something today

What feels most stuck right now?

It does not have to be the perfect choice. Pick the most urgent one.
If you want to keep going

Your problem may need more than one course

Start with the few closest to your situation. Open the full list only when you need to compare.

My work is closer to
The urgent problem is

Start with these

16 paths relate to your choice. The first few are better starting points.

AI Productivity

Spend less time revising emails, capturing meetings, and sorting research

Non-TechBeginner2-4 weeks

Use ChatGPT and Claude for drafting, summarising, research, and spreadsheet work while learning how to check the result.

See what to do first

Prompt Master

State the requirement clearly before another round of follow-up

GeneralBeginner2-3 weeks

Practise adding context, examples, output formats, and checks, then save the useful prompts as work templates.

See what to do first
NEW

AI Builder

Shrink the idea into something another person can try

GeneralIntermediate4-6 weeks

Find the one action users need most, then turn it into a clickable prototype with low-code or no-code tools.

See what to do first
NEW

AI Engineer

Connect APIs, RAG, and agents in one project

TechAdvanced8-12 weeks

Start with model calls, then add private-data retrieval, agent tools, and deployment while explaining each trade-off.

See what to do first

AI Content Creation

Stop restarting from a topic and a blank document

GeneralBeginner3-4 weeks

Start with the audience and a point of view, then produce copy, visuals, scripts, and platform adaptations in your own voice.

See what to do first

AI Data Analysis

Find an actionable answer in a messy dataset

GeneralIntermediate4-6 weeks

Use AI to clean data, generate analysis code, and build charts while checking definitions, outliers, and conclusions.

See what to do first
AnthropicThinking about a Claude credential?

Start with the work you actually do

Using Claude for everyday work, integrating it into an app, designing an agent system, and owning production delivery lead to different exams. Pick the one closest to your current role. Partner Academy access is required for exam registration.

CCAO-F
Knowledge work

Associate Foundations

Output evaluation and validationWorkflow, governance, and responsible use
See what this exam covers
CCDV-F
Application development

Developer Foundations

Applications, integration, and APIsAgents, MCP, security, and safety
See what this exam covers
CCAR-F
Solution architecture

Architect Foundations

Agentic architecture and orchestrationClaude Code, MCP, and context reliability
See what this exam covers
CCAR-P
Production leadership

Architect Professional

RAG, integration, and system architectureEvaluation, governance, and stakeholder delivery
See what this exam covers

You do not need to take all four in sequence. They are choices for different roles, not four levels.

Browse all certifications
Understanding is not the same as doing

A safe place to get it wrong

No environment setup first. Run it, inspect the result, and change it once when it fails.

HOT

Prompt Lab

Write the same task several ways and compare how the answer changes

Stages5
Duration20h+
Role PromptingJSON OutputFew-shotCoT Reasoning
Open the lab and try once→
HOT

Vibe Coding Lab

Turn a fuzzy idea into requirements, then ask AI to generate a project that runs

Stages6
Duration6h
CursorClaude CodeContext EngineeringFull Stack
Open the lab and try once→
NEW

LLM Lab

Call a model, connect your own material, evaluate the answer, and then deploy

Stages6
Duration20h+
RAG SystemsFine-tuningModel EvaluationProduction Deployment
Open the lab and try once→
Come back when you are stuck

Reference material you do not need to finish

Open the right resource when you need an order, a walkthrough, or one command.

Learning Roadmaps

Use when you cannot see what comes first

Knowledge Base

Look up a concept, tool, or specific method

Cheat Sheets

Quickly find a command or pattern you forgot

E-Books

Read through one topic when you want more depth

You may still be hesitating

These concerns are normal

Here are honest answers to the questions that most often stop people from starting.

Can I learn AI without a programming background?
Yes, but you do not need to begin with the goal of becoming a programmer. Start with AI Productivity to reduce repetitive work, Vibe Coding to make a webpage or small tool, or Prompt Master when AI keeps misunderstanding the task. Learn code more systematically only when your project genuinely needs it.
There are so many courses. How do I know I chose the right one?
You do not need to choose the next six months perfectly. Choose the problem that matters today. Repetitive work points to AI Productivity; a product idea points to AI Builder or Vibe Coding; a technical role change points to AI Engineer; inconsistent publishing points to AI Content. Finish one exercise and switch if it does not fit.
Work is already busy. How much time do I need each week?
Start with 15 to 60 minutes for one small task instead of promising yourself 12 weeks. If it solves a real problem, continue with roughly 3 to 5 hours a week. Productivity and prompting can become useful quickly; Vibe Coding and AI Engineer need more continuous practice because projects include code, debugging, and deployment.
I only want to build a product. Do I have to become an AI Engineer?
Not necessarily. Vibe Coding is better for making pages, flows, and testable prototypes first. AI Engineer is for people who need to own LLM APIs, RAG, agents, evaluation, and deployment. Move deeper when your prototype runs into data, reliability, or scale problems.
What is the difference between free resources and paid courses?
If you are still choosing a direction, use the free material first and do not rush to pay. Paths, videos, cheat sheets, Wiki guides, and selected labs are enough to test your interest. Consider a bootcamp when you need a fixed pace, project feedback, mentor support, or help with resumes and interviews.
Can I get a job after learning AI?
No course can guarantee a job. Employers ultimately look for evidence that you can solve a problem. Technical candidates need AI Engineer, RAG, or agent projects they can run and explain. Non-technical candidates need real before-and-after work examples, not just proof that they studied AI. The Interview Prep Center, resume support, and mock interviews can help you present that evidence.
I have saved plenty of AI tutorials before. Will this be any different?
The difference is not more content. It is what you do first. Instead of asking you to browse the full catalogue, the page starts with one short lesson and one exercise tied to a work, product, AI engineering, or content problem. Explanations are in Chinese with the English technical terms you will need later, and the template, prototype, or code can continue into real work or a portfolio.

AI Learning Hub - The #1 AI Learning Platform

JR Academy AI Learning Hub organizes paths around four outcomes: working faster, building AI products, becoming an AI engineer, and creating content. Each outcome connects lessons, interactive labs, roadmaps, videos, wiki guides, and cheat sheets.

AI Learning Directions

  • Vibe Coding: Build full-stack apps with natural-language coding using Cursor, Claude Code, and GitHub Copilot, beginner-ready in 4-8 weeks
  • AI Engineer: Build production AI systems with RAG, AI agents, LLM APIs, and MCP protocols
  • Prompt Master: Prompt engineering frameworks, reusable templates, and chain-of-thought methods for everyone
  • AI Productivity: Use ChatGPT/Claude for writing, translation, slides, and workflow automation
  • AI Data Analysis: Natural-language analytics, Pandas AI workflows, and visualization
  • AI Finance & Accounting: Invoice structuring, auto bookkeeping, reconciliation, and forecasting
  • AI Content Creation: Build image/video/text workflows with tools like Sora and Midjourney
  • AI Builder: Rapidly build AI product prototypes and MVPs with low-code/no-code tools
  • AI Product Manager: AI-native product strategy, requirement analysis, and delivery practice

Recommended Learning Routes

  • Non-technical beginners: AI Productivity → Prompt Master → AI Content Creation (2-6 weeks)
  • Beginners who want coding: Vibe Coding → Prompt Master → AI Builder (4-10 weeks)
  • Developers leveling up: Vibe Coding → AI Engineer → project practice (8-12 weeks)
  • Product/founder track: Prompt Master → AI Builder → AI Product Manager (6-10 weeks)

Supporting Resources

  • Learning roadmaps that show the skill order from beginner to advanced
  • Video tutorials covering tools, demos, and skill explanations
  • Cheat sheets for quick reference across languages, frameworks, and tools
  • Wiki knowledge base with structured technical docs and study notes
  • Interactive labs: Prompt Lab + Frontend Lab for hands-on online practice