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AI models change every few weeks. Don’t let that churn become your team’s problem too. The real disruption isn’t only the big vendor releases, it’s just as likely to hit the moment your own team tweaks a prompt, adjusts a temperature setting, or swaps the weights on a model you’re already running.
Most of the ROI here doesn’t come from chasing frontier-level features. It comes from the habits and processes that carry over cleanly from one model to the next, so a swap becomes a routine event instead of a fire drill.
This panel brings together engineering leaders who’ve built systems that withstand rapid model turnover. They’ll cover how to keep your workflows stable while the model underneath keeps changing, how to test and roll out a new model without a leap of faith, and how to make sure upgrades actually save your team time instead of costing it.
You’ll learn how to:
- Build an abstraction layer that separates your application from any single model
- Test and evaluate a new model before committing to its use
- Adapt your review process to catch the new bugs a model swap introduces
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