Curriculum

Two programs. Built week by week.

A structured curriculum designed around real projects. Every week adds one layer — so by the end of each program, you have a complete, working application you built yourself.

Smart Notes Assistant — 12 weeks • Agentic AI Development — 12 weeks

01 — Approach

What every doors2ai program includes

A guided learning experience designed to help you build real applications with structured teaching, hands-on practice, and one end-to-end project per program.

Learning Format

Live weekend sessions, hands-on coding exercises, a guided end-to-end project, and structured weekly progression.

Teaching Approach

Strong fundamentals first, learning by building, visual explanations, and thinking before tools.

Mentorship

Code reviews and feedback, guided project support, help when you get stuck, industry-oriented best practices.

Outcomes

Build a real application, understand how it works end to end, and create a portfolio-ready project.

Program 1 — September 2026

Smart Notes Assistant

Build a complete AI-powered notes application from scratch — week by week. Covers web fundamentals, interactive JavaScript, backend APIs, database integration, and AI summarization.

Beginner-friendly • Live weekend cohort • Portfolio project • Founding cohort rate

Program structure

Phase 1 — Foundations

Weeks 1–3 • 6 topics
  • How modern web applications work
  • HTML fundamentals and page structure
  • CSS fundamentals and styling
  • Responsive layouts with Flexbox
  • JavaScript basics (variables, functions)
  • DOM interaction and events

Phase 2 — Interactive Frontend

Weeks 4–6 • 5 topics
  • JavaScript DOM interaction
  • Handling user input and events
  • Rendering notes on the page
  • Editing and deleting notes
  • Building a functional notes interface

Phase 3 — Backend Development

Weeks 7–8 • 6 topics
  • Python fundamentals for backend development
  • Conditions, loops, and functions
  • Introduction to JSON and data exchange
  • FastAPI fundamentals (GET and POST APIs)
  • Building simple backend APIs
  • Connecting frontend to backend APIs

Phase 4 — Database Integration

Week 9 • 3 topics
  • Database fundamentals (tables, rows, columns)
  • Basic SQL (insert, select)
  • Integrating SQLite with FastAPI

Phase 5 — AI Integration

Weeks 10–11 • 5 topics
  • Introduction to AI and LLM APIs
  • Sending user input to an AI API
  • Receiving and displaying AI responses
  • Adding AI summarization to the notes app
  • Improving the AI feature with clear instructions

Phase 6 — Final Project & GitHub

Week 12 • 3 topics
  • Completing the Smart Notes Assistant
  • Code organization and project structure
  • GitHub basics: repo, commit, and push

Tools you will use

HTML • CSS • JavaScript • Python • FastAPI • SQLite • AI API • GitHub

What you will build

  • Responsive web interface
  • Interactive notes features
  • Backend APIs with FastAPI
  • Database-connected notes app
  • AI-powered Smart Notes Assistant
  • GitHub-ready portfolio project

Outcome

Build your first Smart Notes Assistant and prepare it as a GitHub-ready portfolio project.

Program 2 — January 2027

Agentic AI Application Development

Build a production-ready AI Document Assistant from scratch — no prior programming experience required. Every week adds one new capability to the same application, from Python basics through to multi-agent AI workflows, live deployment, and a GitHub portfolio project.

Beginner-friendly • No prior programming experience required • Live weekend cohort • Founding cohort rate

One application. Every week. New skills. New capabilities.

Week 1 — Python Fundamentals

Milestone: Project setup and foundational Python skills
  • Variables and data types
  • Functions
  • Lists and dictionaries
  • Loops
  • File handling

Week 2 — FastAPI Fundamentals

Milestone: Backend API for the AI Document Assistant
  • REST APIs
  • GET & POST endpoints
  • Request validation
  • JSON
  • API testing

Week 3 — LLM Fundamentals & Prompt Engineering

Milestone: Generate document summaries using OpenAI
  • How LLMs work
  • Tokens & context windows
  • Temperature
  • Prompt engineering
  • System & user prompts
  • Prompt templates

Week 4 — Retrieval-Augmented Generation (RAG)

Milestone: Ask questions about uploaded documents
  • Chunking
  • Embeddings
  • Retrieval
  • Grounded responses

Week 5 — ChromaDB & Semantic Search

Milestone: Semantic search across uploaded documents
  • Vector databases
  • Collections
  • Similarity search
  • Indexing

Week 6 — LangChain

Milestone: Smarter document processing pipeline
  • Prompt templates
  • Chains
  • Output parsers
  • Document workflows

Week 7 — Streamlit

Milestone: Complete interactive user interface
  • Layouts
  • File upload
  • Chat interface
  • Session state

Week 8 — LangGraph & Agent Foundations

Milestone: Build the Summarization Agent
  • Nodes
  • Edges
  • State
  • Conditional routing

Week 9 — Building AI Agents

Milestone: Summarization Agent, Document Q&A Agent, Document Analysis Agent
  • Agent design
  • Tool selection
  • Agent communication

Week 10 — Agent Orchestration & Deployment

Milestone: Agent Orchestrator built and application deployed on Railway
  • LangGraph orchestration
  • Agent routing
  • Railway deployment

Week 11 — Portfolio & Polish

Milestone: Production-ready application
  • Error handling
  • UX improvements
  • GitHub
  • Documentation
  • README

Week 12 — Final Showcase

Milestone: Present and publish the completed AI Document Assistant
  • Demo
  • Code walkthrough
  • Portfolio presentation

Tools you will use

Python • FastAPI • Streamlit • SQLite • OpenAI • ChromaDB • LangChain • LangGraph • Railway • Git • GitHub

What you will build

  • AI Document Assistant — upload documents, get AI summaries, ask questions
  • Semantic search with ChromaDB
  • Summarization, Q&A, and Analysis Agents
  • LangGraph Agent Orchestrator
  • Live deployed application on Railway
  • GitHub-ready portfolio project

Outcome

A production-ready AI Document Assistant — deployed live, published on GitHub, and ready to showcase in interviews.

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