Last time we explored how GenAI works under the hood. Now let's examine what GenAI can actually do well in software development today, where it still struggles, and what breakthroughs are coming soon.
Current GenAI for coding is impressive but uneven - amazing in some areas, frustrating in others.
Engagement Message
What's your most satisfying experience with AI-assisted coding been so far?
Today's GenAI excels at simple, well-defined coding tasks. Asking GitHub Copilot to write basic functions, generating unit tests, or creating standard CRUD operations works remarkably well.
These systems handle clear requirements in familiar patterns with impressive accuracy - often generating syntactically correct code for common scenarios.
Engagement Message
Which simple coding tasks do you find AI most helpful with?
GenAI also shines in developer productivity applications. It helps with code documentation, explains complex code snippets, and suggests refactoring improvements.
Error debugging assistance, API integration examples, and boilerplate code generation enable faster development cycles and reduce repetitive coding tasks.
Engagement Message
How might GenAI help streamline your current development workflow?
However, GenAI still struggles significantly with complex architecture and business context. It often misunderstands project-specific requirements, forgets existing codebase patterns, and fails with multi-component system design.
Legacy code integration, custom business logic, and domain-specific requirements can drastically reduce accuracy, sometimes producing completely unusable solutions.
Engagement Message
Have you noticed AI generating code that missed important project context?
Security and code quality remain major limitations. Most GenAI can introduce vulnerabilities, suggest outdated practices, or generate code that works but isn't production-ready.
