Unlocking the Future of AI-Driven Applications
I’ve been experimenting with leveraging Large Language Models (LLMs) to generate and iteratively modify small business applications. While the first iterations often succeed, challenges arise in subsequent updates.
Common Issues Encountered:
- Schema drift that disrupts dashboards
- Metrics evolving in meaning over iterations
- Incompatible queries in UI components
- Local fixes that violate global invariants
A Different Approach:
Most AI app builders view generation as a one-time task, but real applications evolve over time. My exploration redefines applications as runtime models using:
- Structured, versioned JSON/DSL for entities and workflows
- Backend validation for all AI-suggested changes
- UI components linked to semantic concepts instead of raw data
- Proposals from AI enforced by runtime consistency
I’m eager to hear from fellow innovators. Have you explored similar strategies? Let’s discuss!
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