Track 2: AI ImplementationIntermediate LevelSkill 1 of 3

No-Code AI Development

Build custom AI applications without programming using visual development platforms

4 weeks Duration
12-15 hours/week
Multiple Projects

Prerequisites

Required Background

Track 1 completion or equivalent AI literacy

  • Commitment to 12-15 hours/week of study and practice
  • Strong foundation in previous track skills
  • Access to necessary tools and platforms

Learning Outcomes

Upon successful completion of this course, you will be able to:

1

Build functional AI applications using no-code platforms (Bubble, Glide, Softr)

2

Integrate AI APIs and services into custom applications

3

Design user interfaces and workflows for AI-powered apps

4

Connect AI tools to databases and external services

5

Deploy and maintain no-code AI applications

6

Troubleshoot common issues in no-code AI development

Content Outline

Module 1: No-Code AI Application Development (Weeks 1-4)

This module introduces students to no-code development platforms and teaches them to build functional AI applications without writing code.

Week 1: No-Code Development Fundamentals
  • Introduction to no-code/low-code platforms: Capabilities and limitations
  • Platform selection: Bubble, FlutterFlow, Glide, Softr comparison
  • UI/UX basics: Designing user-friendly interfaces
  • Database fundamentals: Tables, relationships, and data modeling
  • User authentication and access control
  • Project: Build a simple web application with user login
Week 2: Integrating AI APIs
  • Understanding APIs: How applications communicate
  • OpenAI API: GPT models, pricing, and implementation
  • Anthropic Claude API: Features and use cases
  • API authentication: Keys, tokens, and security
  • Making API calls from no-code platforms
  • Project: Create a custom AI chatbot with personality and context
Week 3: Building AI-Powered Applications
  • Application architecture: Frontend, backend, and AI layer
  • Prompt management: Storing and versioning prompts in applications
  • Context management: Maintaining conversation history and state
  • Error handling: Graceful failures and user feedback
  • Rate limiting and cost management
  • Project: Build an AI writing assistant web application
Week 16: Deployment Week 4: Deployment & Production Readiness Production Readiness
  • Testing AI applications: Functional and user acceptance testing
  • Performance optimization: Response times and user experience
  • Deployment process: Moving from development to production
  • Custom domains and professional branding
  • Monitoring and analytics: Tracking usage and performance
  • Project: Deploy a complete AI application to production

Ready to Master No-Code AI Development?

Join the I-Gamify AI Academy and advance your AI expertise. This course is part of Track 2: AI Implementation.