Verified by Fetch Hive
For analysts, product managers, and operators choosing among tested AI models, this template compares pasted quality, cost, latency, and failure evidence against a buyer priority. It produces a one-page decision memo with transparent scores out of 100, a recommendation, cited trade-offs, caveats, and a first re-test.
How it works
Paste one row per tested model, its failure examples, your top priority, and the date for the memo. The workflow extracts row-level evidence, calculates explainable priority-aware scores out of 100, then writes a one-page recommendation with references to the pasted rows and examples, no external model research.
Good for
Analysts, product managers, and operators selecting from their own AI model tests.
What runs, step by step
Provide your input
Model test results table, Failure examples by model, Top buyer priority or constraint, and Memo date (today's date)
Capture test evidence
Identify model rows and verbatim failure examples
Score eligible models
Apply the buyer's priorities to explainable 100-point scores
Write buyer decision memo
Present the cited recommendation and first re-test on one page
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