Define the job before judging the tool
Model comparisons often begin at the wrong end. A benchmark announces a winner, a feature list follows, and only then does someone ask what the system needs to do. Reverse that order.
Start with the work unit: summarize a meeting, classify creator profiles, generate headline directions, extract brief requirements, or draft a response for human review. Then define an acceptable output, the facts it must never invent, and the person authorized to approve it.
Construct a representative evaluation set before selecting the model. Include routine examples, boundary conditions, adversarial phrasing, missing context, and cases that should be refused or escalated. Compare against a simple non-model baseline; otherwise an impressive demo may conceal that rules, search, or a template solves the task more predictably.
Run a five-card review
Use five cards to keep a model test grounded. Record examples beside every score. “Good” is not a finding; “missed two exclusions in three varied tests” is. A short evidence log makes comparisons possible when models, prompts, or requirements change.
Disaggregate the score. Task accuracy, instruction adherence, calibration, latency, token cost, and false-positive or false-negative consequences answer different questions. A single average can hide a failure mode that is rare in the test set but expensive in production.
- Task fit
- Instruction fit
- Review burden
- Operating cost
- Failure shape
Count the complete workflow
Generation speed is only one line in the ledger. Add preparation, context gathering, human review, correction, approval, delivery, and recordkeeping. An apparently inexpensive model can become costly when its drafts require extensive checking.
Treat the reviewer as part of the system design. Specify what that person checks, what evidence they need, and when an output must be rejected rather than repaired.
Measure review burden with the same discipline as inference cost: time to verify, proportion of outputs requiring reconstruction, escalation frequency, and the cognitive difficulty of detecting a plausible error. Include privacy constraints, context-window pressure, tool permissions, and version drift in the operating assessment.
Owner’s workbench
Recollective offers AI-assisted growth strategy and practical, human-reviewed workflow design. Its first-party proposition is not “let the model run everything,” but to identify useful applications, define boundaries, and keep accountable people in the loop.
That service description does not establish a universal model winner or guarantee results. The review framework above remains useful precisely because it asks teams to test their own work.