Start with the consequence
Choose a question a manager might act on. Check the business, dates and source before treating an answer as useful.
CASE 02 / AI evaluation
Toast IQ: making an answer useful enough to act on
Source-based evaluation and clear operating context for product decisions.
DELIVERED
Completed source comparisons and a bounded natural-language item study; delivered reproducible operator feedback.
EVIDENCE LIMIT
Product acknowledgment, a passing source comparison and a shipped fix are different results. No attributable release or measured business improvement is established.
Okay, but does that answer hold up?
I run a restaurant/bar, a coffee shop and a bakery that supplies other Spill the Beans locations. Those are three different operations, even when the information lands in the same reporting picture. So when AI gives me a tidy answer, my next question is: does this actually describe the business and the decision I’m looking at?
What I owned.
I chose questions with operating consequences, checked the answers against POS reports, schedules and manager context, and packaged repeatable findings for product-team investigation. In a natural-language item study, I evaluated item recognition, date scope and numerical agreement separately. My role was external customer, operator and evaluator.
Where I used judgment.
Separate the failure modes
Grade item recognition, date scope and arithmetic separately. A correct quantity for a different window can still fail the intended task.
Give the team a repeatable case
Preserve controls and expected versus observed behavior. Recheck the same scenario before declaring it resolved.
Who had to be in the loop.
I owned the operating question, source comparison, reproduction steps and follow-up criteria. Toast’s product and engineering teams owned diagnosis and product changes. Manager observations helped explain the business context. I prioritized discrepancies that could change a staffing, reporting or purchasing decision over cosmetic differences.
One item. A few separate checks.
This invented example illustrates the method behind my natural-language evaluation work. “House cold brew” and the example question are fictional. No actual Toast output or private finding is reproduced.
Did we reach the intended item?
Example question: “How many cold brews did we sell this week?” First check whether the system and the operator mean the same product. Try ordinary plural wording and an exact-name control.
Are we comparing the same period?
Hold the business, date range and treatment of a partial day constant. Separate a scope mismatch from an arithmetic disagreement.
Can the result be checked?
Compare the stated result with an independent report using the same filters and units. An absent-item control should lead to a useful clarification rather than an invented quantity.
My actual contribution: structured source comparisons, a bounded item-language study and reproducible operator feedback. Detailed findings and private product conversations stay private. No product fix or business lift is claimed.
What was actually delivered.
- Documented source comparisons and reproducible operator feedback for product-team investigation.
- A bounded natural-language item study with separate recognition, date-scope and report-comparison checks.
- Follow-up criteria that distinguish a useful test result from sustained reliability. Detailed product findings remain private.
What remains to demonstrate.
Complete the remaining repair trial, keep comparison conditions fixed and recheck source evidence. Assess independent manager use separately from a successful answer.
I can get a technically correct number and still have no useful next step. The best feedback I can give a product team explains the decision I was trying to make, what I checked and where the answer stopped helping.