AI-assisted practical guide. Examples are hypothetical; proposed workflows are editorial suggestions.
A small evaluation set can make an AI writing trial more informative than comparing whichever outputs happen to impress you. Choose examples that represent the actual editing task and define what counts as acceptable before running them.
Build a varied task packet
Include a straightforward input, an ambiguous one, an input with missing evidence, and a case where an exception must survive the rewrite. Use fictional material when possible. Keep the original inputs unchanged across comparisons.
For each case, write observable checks: required facts preserved, unsupported claims absent, requested format followed, and uncertainty retained. Avoid a single “looks good” score.
Use a hypothetical failure
Imagine a rewrite improves tone but removes the sentence limiting an offer to existing customers. Record the failure even if the rest reads well. The missing exception may matter more than stylistic improvement.
Keep the output, prompt, model identification when available, and date. If you change the prompt, rerun the same examples rather than comparing a new prompt on easier inputs.
Interpret the result narrowly
Report what happened on this packet and how much review was required. Do not turn a handful of successful examples into a universal accuracy claim. The evaluation is useful when it reveals where the workflow needs checking and provides a repeatable basis for the next decision.