• Software developers, Full-stack, backend, or frontend engineers
• Technical leads or senior IT executives.

Modern software development increasingly involves collaboration between human engineers and AI systems. Tools such as Claude Code make it possible to generate code, explore architectural ideas, integrate APIs, and debug complex systems faster than traditional development cycles allow. However, effective use of AI in development requires more than simply asking an AI model to produce code. Developers must learn how to guide AI systems, review generated outputs, refine architecture decisions, and maintain ownership of code quality throughout the process.

Day 1: From Idea to a Working AI Application Core

Session 1: Project Setup & AI-Assisted Scaffolding

• Understand how developers can guide Claude Code to generate a complete project scaffold from a plain-language product brief, including folder structure, dependencies, and mobile-first configuration.

• Learn how to review and refine AI-generated architecture to ensure the resulting project structure remains maintainable and aligned with professional development standards.

Session 2: Rapid Feature Generation User Preference Onboarding

• Learn how Claude Code can translate functional requirements into working application features such as onboarding flows and user preference profiling interfaces.

• Understand how developers can evaluate, refine, and safely integrate AI-generated code within an existing application codebase.

Session 3: External Data Integration Location & Places API

• Explore how Claude Code can assist in researching and integrating third-party APIs, including evaluating service options and implementing authentication and error handling.

• Learn how to implement location-aware features such as GPS detection and manual search fallback to support mobile user experiences.

Session 4: Building the AI Recommendation Engine

• Understand how applications integrate with large language models to deliver intelligent features such as personalized recommendations.

• Learn how structured prompts and response parsing enable AI systems to combine user preference data with live location inputs to produce ranked travel suggestions.

Day 2: Intelligence, Reliability & Product Readiness

Session 5: Agentic Features Dynamic Itinerary Generation

• Learn how multi-step AI reasoning workflows can generate structured travel plans by coordinating multiple inputs such as location, preferences, and scheduling constraints.

• Understand how AI-generated plans can be rendered as intuitive, mobile-optimized user interfaces such as timeline-based itineraries.

Session 6: Advanced Prompting & Robust AI Behavior

• Discover how AI features behave under real-world edge cases, including sparse data, conflicting user preferences, or unfamiliar locations.

• Learn how fallback logic, validation, and improved prompt design can make AIdriven features more reliable in production environments.

Session 7: Testing, Documentation & Code Quality

• Understand how Claude Code can accelerate test creation and help developers maintain high confidence in complex application logic.

• Learn how AI can assist in generating developer documentation and user guides while maintaining accuracy and clarity.

Session 8: Final Product Polish & Deployment Preparation

• Learn how to finalize mobile user experiences by implementing features such as interactive maps, progressive web app (PWA) configuration, and offline functionality.

• Understand how developers can review, demonstrate, and refine their AI-assisted development workflows to apply them in future projects.