Effective AI-Powered Software Development

If you want to go fast, go alone.
If you want to go far, go together.
If you want to go far fast, go AI!

Unlock 4x productivity for your software team!

This hands-on 1 day workshop teaches you how to leverage AI for code generation, debugging, documentation, and more. Perfect for startups and tech teams looking to stay ahead with AI-powered software development.

Workshop Structure

1. Foundations - 60 mins
   • History of coding assistants and AI
   • LLMs: How they're trained, how they work and reason
   • LLMs vs. traditional code completion
   • Strengths: code completion, boilerplate generation, debugging assistance, documentation, test cases, data mapping
   • Limitations: complex architecture decisions, business logic, security considerations, compliance, performance optimization
   • When to trust AI suggestions vs when to be skeptical
   • Realistic expectations in a startup context (speed vs. correctness vs. maintainability)
2. Let’s get Practical - 60 mins
   • State of the Art and choosing the right tools
   • Workspace setup and configuration
   • Prompt engineering basics for code generation
   • The "AI as junior developer" mental model
   • Context building techniques
   • Team coding standards and how to teach them to AI
   • Advanced Prompting: few-shot, zero-shot, and step-by-step prompting
   • Prompt chaining, Modular prompting, System prompts
   • Embedding AI in coding workflows: from brainstorming, to prototyping to doc writing to test generation
   • Using AI as a pair programmer vs. code oracle
   • Code Agents, MCP tools and “Vibe” coding
   • Code generation, refactoring, debugging
   • Test case generation, edge case identification
3. Pitfalls, Long Term Thinking & Next Steps - 60 mins
   • Over-reliance: degradation of internal problem-solving and debugging ability
   • Hallucination and “confident nonsense”
   • Maintaining architectural consistency and code quality across the team
   • AI-generated technical debt
   • Code review considerations when AI is involved
   • Building verification habits: Always test business logic manually
   • Using your domain expertise as the ultimate validator
   • Debugging AI bugs: tracing back from subtly wrong assumptions
   • Performance considerations
   • Licensing and copyright concerns
   • Security implications: injection, data leaks, dependency issues
   • Misalignment with startup goals: premature optimisation, feature creep
   • When to switch off AI?
   • Team adoption strategy
   • Measuring Success and RoI

Hands-on Sessions

• Session A: Building a CRUD App with AI Assistance - 60 mins
• Session B: Refactoring a Legacy Codebase - 60 mins
• Session C: Debugging, Edge Cases, and Testing with AI - 60 mins
• Session Z: Open house - 60 mins

Handouts / Printables

• Prompting Cheat Sheet for Developers
• AI Code Review Checklist (semantic + syntactic + security)
• Hallucination Detection Guide
• List of Good Questions to Ask AI
• Cursor AI settings optimized for your platform
• Code style configurations

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