Define success before comparing models.
Write down what the output must contain, what errors matter and which conditions should cause the system to ask for help. For a reporting task, that might include correct calculations, current sources, a clear explanation of exceptions and a usable review format. Agree how reviewers will score each requirement so different providers or workflow versions face the same standard.
Build a representative case set.
Collect approved examples that reflect routine work, difficult exceptions and the information gaps your team encounters. Keep a separate evaluation set when adjusting prompts or generating synthetic examples, so repeated exposure does not make the comparison misleading. For Canadian deployments, include relevant language, terminology and document formats when those are part of the intended operating scope.
Check evidence and action boundaries.
Verify whether a source actually supports a generated claim and whether the system distinguishes missing evidence from an answered question. If the workflow can use tools, inspect the inputs it sends, the access checks applied and the result it records after an action. Test cases where a document or incoming message contains instructions that conflict with the workflow's authorized task.
Include failures and the human workload.
Run scenarios with unavailable tools, incomplete records and ambiguous requests to see whether the system stops, retries appropriately or escalates with useful context. Measure the time reviewers spend checking and repairing outputs alongside response time and provider cost. A dependable workflow should leave an operator able to understand an exception and continue the job without reconstructing the entire run.
Use findings to make a bounded decision.
Record the tested configuration, case coverage, results and limitations so someone else can understand what the evaluation establishes. Decide whether the evidence supports a pilot, a narrower scope, another experiment or an operational rollout. Active K Digital can help build evaluation material and compare models, prompts or integrations against your team's acceptance criteria, then preserve those checks for later changes.
Your starting checklist.
- Define acceptable outputs and material failure conditions.
- Use representative cases and a separate evaluation set.
- Check source support, calculations and tool actions.
- Measure reviewer effort, recovery, latency and cost.
- Record the configuration, coverage and remaining limitations.