A coding assistant’s demo may use a clean project with a clearly stated problem. Your repository probably has older conventions, incomplete documentation and behavior that lives outside the obvious files.
Compare assistants on a small set of work from that environment. Judge the resulting change and the effort needed to make it acceptable, not the confidence of the final message.
Choose different kinds of tasks
Include a contained bug fix, a small feature and a change that requires following an existing convention. Add a case where the correct response is to ask for missing information.
Use tasks whose expected behavior you can verify independently. If a test is missing, define the observable result before running the comparison.
Keep the starting state identical for each assistant. A tool that sees another tool’s solution or a different set of uncommitted changes is not receiving the same task.
Give the same project context
Provide the relevant instructions, setup requirements and permitted scope. Make clear which files or systems should not change.
An assistant can fail because it misunderstands the task or because the environment is incomplete. Record the difference. A missing dependency is a setup problem; claiming a successful test that never ran is a reliability problem.
Anthropic’s discussion of agent evaluations emphasizes judging the final environment state as well as the observed run. That principle applies directly to coding tools.
Inspect the diff, not just the summary
Read every changed file. Look for unrelated edits, weakened checks, duplicated logic and changes that solve only the demonstrated case.
A test added by the assistant can be useful, but it is not independent evidence if it merely repeats the implementation’s assumptions. Check the intended behavior through the project’s normal verification route.
For a visible interface change, open the interface. A successful build does not prove that the layout works on a phone or that a saved value persists after reload.
Record intervention and cleanup
Track how many times you redirect the tool, provide another file, reject an approach or repair the output manually.
Also record time spent reviewing. A larger change can look productive while creating more uncertainty than a smaller, focused implementation.
Our AI workflow cost guide explains how review and retries can dominate the request price. For coding work, maintenance burden belongs in that comparison too.
Compare permissions and evidence
Some assistants only propose text. Others can run commands, access services or modify remote systems. The useful permission set depends on the task.
Check whether the tool makes consequential actions visible and whether its reported test results match actual output. It should distinguish a local check from a deployed result.
Use isolated, disposable data for the trial. A benchmark does not need production records or broad access to unrelated accounts.
Repeat after a meaningful change
A single run can be unusually good or bad. Repeat important tasks and retain the results with the model, tool version and relevant settings where available.
When the product changes, rerun the same core set. Add new tasks that represent failures you encountered in ordinary use, while keeping some examples separate from prompt tuning.
The result may be a division of work rather than one universal winner. One assistant may be easier to review for small edits, while another handles a longer investigation more effectively.
