QUBE Elevate
AI Integration for Developers
Master practical AI tools and frameworks to elevate your development workflow and build intelligent applications
Why AI Integration Matters
Modern developers need to leverage AI tools to stay competitive and productive
Development Bottlenecks
Developers spend countless hours on repetitive tasks: writing boilerplate code, creating documentation, debugging issues, and maintaining test coverage. Traditional development workflows are inefficient and time-consuming.
"80% of development time is spent on maintenance, debugging, and documentation rather than building innovative features."
AI-Powered Development
Transform your workflow with AI:
Automated Code Generation:
AI assistants write, review, and optimize code with minimal manual effort
Intelligent Documentation:
Generate comprehensive docs, tests, and diagrams automatically
Master AI Development
Build expertise across the essential areas of AI-integrated development workflows.
AI Development Tools
Master GitHub Copilot, ChatGPT, and other AI coding assistants for enhanced productivity
Automated Testing
Generate comprehensive unit tests and improve code coverage using AI assistance
Documentation & Design
Create automated documentation, diagrams, and system designs with AI tools
Production Deployment
Deploy and maintain AI-powered tools and services in production environments
AI Integration Learning Path
From prompt engineering to production deployment - master the complete AI development workflow.
Prompt Engineering & LLMs for Developers
AI-Powered Development
Key Topics:
- Effective prompting techniques
- AI pair programming workflows
- Debugging and code generation with Copilot, ChatGPT
- +1 more topics
Tools:
Outcome:
Team writes and reviews code using LLMs with minimal manual effort
AI-Generated Documentation & Diagrams
Automated Documentation
Key Topics:
- Auto-generate docstrings and READMEs
- Create OpenAPI specifications with AI
- System design with GPT assistance
- +1 more topics
Tools:
Outcome:
Create documentation and architecture diagrams for existing modules
Unit Testing with AI
AI-Driven Testing
Key Topics:
- Generate unit tests from code and comments
- Interpret and improve AI-generated tests
- Edge case coverage analysis
- +1 more topics
Tools:
Outcome:
Add 80%+ unit test coverage to key modules using AI
AI-Driven Code Reviews & Risk Analysis
Quality Assurance
Key Topics:
- Generate PR summaries and review comments
- Security checks with AI assistance
- Static analysis and code quality
- +1 more topics
Tools:
Outcome:
Review real PRs using AI; flag risks and code smells effectively
RAG & Automation Pipelines (Intro)
Advanced AI Systems
Key Topics:
- Understanding Retrieval-Augmented Generation
- Build RAG systems with internal docs
- Prompt chaining fundamentals
- +1 more topics
Tools:
Outcome:
Set up a prototype AI code assistant or internal doc bot
Intro to ML/LLM Fine-tuning (Foundations)
Model Customization
Key Topics:
- ML pipeline basics and concepts
- Understanding embeddings and vectors
- Fine-tuning small models
- +1 more topics
Tools:
Outcome:
Fine-tune or embed internal docs for simple Q&A tasks
Deploying Internal AI Tools
Production Deployment
Key Topics:
- Host LLM and RAG bot systems
- Set up internal AI endpoints
- Performance optimization and scaling
- +1 more topics
Tools:
Outcome:
Deploy an internal AI agent (e.g., PR bot or QA bot)