AI-Powered Development: Hands-On Techniques for Immediate Impact by Lize Raes

AI models are becoming increasingly capable at code generation, but work best for standard/boilerplate code rather than complex, interconnected systems

Key AI development tools currently available:

• GitHub Copilot and similar coding assistants
• ChatGPT for code generation
• Cursor (VS Code clone with AI features)
• CodeRabbit AI for automated PR reviews
• Langchain4j and similar frameworks for LLM integration

Documentation and testing are areas where AI can have major impact:

• Automated test data generation
• Unit test creation
• Documentation updates and maintenance
• Converting specs into test cases

Important considerations when using AI for development:

• Review and validate AI-generated code
• Be aware of limitations with complex, multi-class changes
• Consider data privacy when using cloud-based models
• Local/private models available but less capable
• Costs can be significant with large context windows

Future trends and implications:

• Models getting smaller while improving in quality
• Local/on-device AI becoming more feasible
• Programming may shift toward natural language by 2030
• AI likely to augment rather than replace developers
• Focus will shift to higher-level problems as routine tasks are automated

Practical applications emerging:

• Converting web forms to AI chatbots
• Automated code review and analysis
• Intelligent search across documentation
• SQL generation from natural language
• Function calling and agent-based development

State management and context handling are crucial when building AI-powered applications

Models work best when properly scoped and prompted for specific tasks rather than trying to solve everything