About the course
AI Basics isn't a course about AI theory — it's a practical skill: how to think alongside AI agents, give them clear tasks, and carry an idea through to a finished product. No IT background needed — every concept is explained in plain language and reinforced with hands-on practice every session. Across 11 topics and 15 sessions (twice a week, 2 hours each, about 8 weeks total) you go from your very first prompt in a chat window to your own application with a database, a bot, and a real production deployment. The course is built around Claude — Chat, Cowork and Claude Code — as its main toolkit, but the thinking and principles behind working with AI agents carry over to any modern AI ecosystem.
Course Syllabus
What you'll learn, step by step.
Session 1
- What artificial intelligence and Generative AI actually are
- How large language models (LLMs) work
- What tokens are
- Context and the context window
- How a model "understands" a request
- What a prompt is
- The structure of a good prompt
- The role of instructions, context, and examples
- Limitations of today's AI models
- What an AI Agent is
- What Vibe Coding is
- How Vibe Coding differs from regular programming
- Where AI helps a developer — and where you can't fully trust it
Session 1
- Claude Chat
- Claude Cowork
- Claude Code
- When to use Chat, Cowork, or Code
- Working with files and documents
- Projects
- Skills
- Deep Research
- Working with large amounts of information
- Limitations of AI interfaces
Session 1
- Installing Claude Code
- The terminal and basic commands
- Running Claude Code in the terminal
- Creating a project from scratch
- Analyzing an existing project
- Working with files
- Running the application
Session 2
- Working with Git
- Fixing bugs
- Building new features
- Your first deployment
- How to give the agent clear tasks
- How to verify AI's work
- How to "manage" Claude Code
Session 1
- What the context window is
- Why context runs out
- How bad context degrades results
- How to avoid "cluttering" context
- /clear
- /compact
- New session vs. continuing an old one
- Short sessions vs. long sessions
- What AI memory is
- What to save — and what not to
- How to organize long-term work on a project
- Project context vs. project memory
Session 1
- What Markdown files are
- What CLAUDE.md is
- CLAUDE.md as the project's "brain"
- What to store in CLAUDE.md
- Project architecture
- Development rules
- Coding conventions
- Project commands
- Restrictions and things to avoid
- Global setup
- Project-specific instructions
- Keeping CLAUDE.md up to date
- Iteratively improving your instructions
- Beyond the basics
- gstack / gbrain
- AI instructions and workflow
- Standardizing how an AI agent works
Session 1
- How AI understands design
- Design systems
- Color systems
- Typography
- Spacing
- Components
- UI patterns
- Responsive design
- How to describe an interface to AI
- How to get quality UI instead of "typical AI design"
- Iterating on a design
- AI as a UI/UX partner
Session 1 — Product and Frontend
- What an MVP is
- How to define your MVP
- What NOT to build at this stage
- User stories
- Core user scenarios
- Application architecture
- Frontend
Session 2 — Backend and Data
- Backend
- Database
- Authentication
- CRUD
- Supabase
- How AI works with a database
- Designing a database schema
- Relations
- Security
- Authentication / Authorization
Session 1
- What Skills are
- SKILL.md
- How to build your own Skills
- When you actually need a Skill
- Connectors
- Connecting Google Drive
- Gmail
- Calendar
- What MCP is
- MCP Server
- MCP Client
- External tools for an AI agent
- Connection security
- AI as an interface to internal systems
Session 1 — RAG Concepts
- The LLM knowledge problem
- What RAG is
- RAG in plain terms
- Documents and knowledge bases
- Chunking
- Embeddings
- Vector databases
- Semantic search
- Retrieval
- Generation
- The RAG pipeline
Session 2 — Practice and Sources
- Your own knowledge base
- Working with external sources
- Tavily and web search
- Building an AI wrapper around search
- Hallucinations
- Verifying sources
- RAG security
Session 1
- What an API is
- REST APIs
- HTTP requests
- API keys
- JSON
- Reading API documentation
- Integrating external services
- Webhooks
- Events
- Telegram bots
- WhatsApp bots
- AI agent + external API
- Error handling
- Rate limits
- Keeping API keys secure
Session 1 — Debugging and Quality
- Iterative development
- Finding problems with AI's help
- Debugging with Claude Code
- Code review with AI
- Refactoring
- Improving UX
- Optimization
Session 2 — Security and Deployment
- Security review
- Checking your API
- Checking your database
- Error handling
- Logs
- Environment variables
- Production configuration
- Deployment
- Monitoring
- Final polish
Leave a Request
Leave your details and our admissions team will call you back to help pick the right course.