AInews · 2026-06-23 · 2 min read
Build for Data, Not for a Single Model
Founder guidance on why durable AI products should be built around data, process, and workflow—not around one model or one library.
When a model changes, your product should not need a complete rebuild. Keep business rules, data access, and quality checks separate from the part that calls the model. That makes it easier to compare alternatives without changing the whole workflow.
Build around the workflow
Start with dependable inputs and a clear definition of a correct output. Keep examples of successful and failed tasks so you can check whether a new model actually improves the product. These checks remain useful even when the provider or implementation changes.
I see a lot of AI products glued to one technology choice. That works right up until it doesn’t. A real product should treat models as tools inside a larger system, not as the system itself. The system should still know what good output looks like even if the underlying model changes next quarter.
What to put in place
In practice, that means a few things:
- clean the inputs before you obsess over the prompt
- define the output in business terms
- keep the workflow stable even if the model changes
- invest in evaluation so you can tell whether quality actually improved
- make sure humans still own judgment and accountability
Make future changes easier
Before switching models, run the same examples through both versions and compare correctness, cost, and review effort. Keep a way to return to the previous version if the new one causes problems. This turns a model upgrade into a controlled change rather than a redesign.
Planning an AI automation initiative? Read the AI automation POC guide.