A cooperative's leadership is comparing two AI vendor pitches for a crop-risk tool. Vendor 1 claims its system "improves with every prediction it makes in the field." Vendor 2 says its model "stays fixed after training unless the team deliberately retrains it with new data." Before signing a contract, what should the program manager evaluate most carefully?

The claim worth digging into is Vendor 1's assertion that the system "improves with every prediction" — models generally don't rewrite their own learned patterns from live inference alone, so the program manager needs to find out whether this really means automatic on-the-fly learning, or whether the vendor is simply describing periodic retraining cycles they run behind the scenes and marketing as continuous improvement. One distractor asks about onboarding requirements for the other vendor's system, which is a logistics question with no bearing on evaluating either vendor's technical claim about how their model learns. Another distractor asks which system produces outputs faster, a performance metric unrelated to whether the underlying claim about automatic learning is even accurate. A third distractor raises pricing structure, a commercial term that says nothing about whether the learning-behavior claim holds up. The core evaluation skill here is treating a vendor's plain-language claim about model behavior as something to verify against how training and inference actually work, rather than accepting marketing language at face value — ask for specifics on retraining cadence and data sources before signing.