Evaluate the task before choosing the model

Model selection becomes easier when the team agrees on the task it is trying to improve. Broad benchmark results can provide context, but they do not describe every organization's documents, terminology, user expectations, or workflow constraints. A task-specific evaluation provides that missing detail.

Begin with permitted examples that reflect the intended use. Include the common requests and the difficult cases the team cares about. Record the information available to the system, the desired result, and the reasons an answer would be considered unacceptable.

Choose criteria a reviewer can apply consistently. For a grounded answer, ask whether its claims are supported by the supplied sources. For extraction, compare each required field. For a draft, assess whether it follows the requested format and whether it introduces unsupported details. Some tasks need several criteria to describe quality.

Keep examples used for iteration separate from a held-out set used for comparison. If every design change is tailored to the same familiar examples, apparent improvement may not transfer to new requests. Add new cases when real use reveals a missing pattern.

Measure operational constraints alongside quality. Record latency, cost, context limits, and how the workflow behaves when a provider is unavailable. Review the provider's relevant data-handling terms before sending information. These constraints can change which option fits the product.

Use human review where a simple automatic score would miss the intended meaning. Record disagreements between reviewers and improve the rubric. Where possible, compare the AI-assisted workflow against the current way people complete the task.

Repeat the relevant evaluation when the model, instructions, retrieval process, or data sources change. The evaluation set becomes a practical regression check for the behavior the product depends on.