AI for Developers
This focused 3-day program gives software developers a practical introduction to AI-assisted software engineering, including prompting, coding, debugging, testing, documentation, RAG, and workflow design.
Duration
3 Days
Overview
This 3-day hands-on program helps software developers use Generative AI as a practical development partner in everyday software engineering work. Participants will learn structured AI-assisted workflows for coding, debugging, testing, documentation, and system design, while applying AI in real development environments with attention to quality, consistency, and security.
Introduction
Generative AI is changing how developers approach analysis, coding, testing, documentation, and delivery. This program introduces a hands-on approach to AI-assisted software engineering, showing participants how to work with AI as a practical development partner. Across three days, participants will build structured prompting habits, use AI in real development workflows, and explore how documentation context, multi-agent collaboration, and system thinking can improve software delivery outcomes.
Topics Covered
- Introduction to AI-Assisted Software Engineering
- AI Workspace and Core Concepts
- Prompt Engineering
- Code Generation and Refactoring
- Building Reusable Skills
- Debugging and Code Analysis
- Test Case Generation
- Documentation and Productivity
- AI in Development Environments
- Workflow Management
- Advanced Prompting
- Introduction to RAG
- Multi-Agent Workflow
- System Design with AI
- Best Practices and Security
- Capstone Project - AI-Assisted Development Pipeline
Audience Profile
Software Developers Technical Leads QA Engineers IT Professionals transitioning into AI-assisted development
Prerequisites
Programming experience in at least one language (Java, JavaScript, Python, PHP, etc.) Familiarity with software development lifecycle and source control practices Basic understanding of APIs, web applications, or modern development tools
Learning Outcomes
- Apply structured prompting techniques
- Use AI for coding, debugging, and testing
- Create reusable prompt templates
- Apply AI in real development environments
- Understand and reduce AI limitations
- Build an AI-assisted development pipeline
Day 1 - Foundations and Prompt Engineering
1. Introduction to AI-Assisted Software Engineering
- Evolution of Generative AI
- Traditional vs AI-assisted development
- Capabilities and limitations
- When to use AI
- AI-assisted software engineering concept
- When to Use AI in Development
2. AI Workspace and Core Concepts
- Projects, Artifacts, Conversations
- Reusable prompt templates (Skills)
- Projects, Artifacts, and Conversations
3. Prompt Engineering
- RCCT framework
- Iterative prompting
- Improving output quality
- Structured Prompts with Role, Context, and Constraints
- Prompt refinement
4. Code Generation and Refactoring
- Generate functions, APIs
- Refactor and optimize code
- Maintain readability
- When AI Code Needs Review or Redesign
5. Building Reusable Skills
- Create prompt templates
- Standardize workflows
- Reusable Prompt Templates
Day 2 - AI-Assisted Development Workflows
6. Debugging and Code Analysis
- Bug detection
- Root cause analysis
- Legacy code understanding
- Handling Partial or Unreliable Outputs
7. Test Case Generation
- Unit and integration tests
- Edge case detection
- Test coverage improvement
- Generate Tests and Documentation
8. Documentation and Productivity
- Generate documentation
- Automate reviews and commits
- Reusable Prompts for Coding, Testing, Debugging, and Docs
9. AI in Development Environments
- IDE integration
- Git workflows
- PR reviews
- Chat, IDE, and API Workflow Differences
- Working Across Multiple AI Tools
10. Workflow Management
- Projects, Skills, Artifacts usage
- Reuse and iteration
- Workflow Management for Repeatable Tasks
- Organizing Prompts and Outputs for Reuse
Day 3 - Advanced AI Usage and System Thinking
11. Advanced Prompting
- Multi-step prompting
- Task decomposition
- Workflow design
- Task Planning
12. Introduction to RAG
- Concept of RAG
- Using documentation as context
- Documentation Context for RAG
- Grounding Responses
13. Multi-Agent Workflow
- Developer, Tester, Reviewer agents
- Simulated team workflow
- Agent Coordination
14. System Design with AI
- Requirement breakdown
- Architecture design
- AI in Architecture and Engineering Workflow
15. Best Practices and Security
- Handling hallucinations
- Secure prompting
- Data safety
- Safe AI Use in Delivery
- Sensitive Data Handling
16. Capstone Project - AI-Assisted Development Pipeline
- Define a real-world feature
- Use AI for design, code, testing, documentation
- Create reusable prompt templates
- Deliver working mini-project and workflow documentation
- Role-Based or Multi-Agent Workflow Design