Test an AI assistant with your own realistic questions
A good demonstration shows what an AI assistant can do under prepared conditions. Your evaluation should show whether it can handle your ordinary work and recover when something goes wrong. Use a small original test set with explicit expected outcomes instead of judging only the most impressive reply.
Write the scoring rules before the test
Choose three tasks that represent your needs and one failure case. For each, define the required facts, acceptable format and disqualifying mistake. A practical scorecard can use pass, partial and fail with a short explanation. Decide what matters before an appealing voice or animation influences the judgment.
For example, if you need reliable next actions, a fabricated owner is a fail even when the summary is elegant. If you need spoken interaction, being unable to make a correction is important even when text quality is high. Keep critical requirements separate from nice-to-have features.
Use a four-part synthetic test set
Test one precise transformation, one open-ended conversation, one context-dependent question and one missing-information request. A transformation might summarize “The draft is ready; testing has not started.” The required outcome preserves the untested state. The conversation might rehearse a short opening for a project review.
For context, supply two options and a deadline, then change one constraint. For missing information, ask who approved a decision when the supplied note names nobody. A useful answer should acknowledge the absence rather than invent approval. These examples are proposed exercises, not published benchmark results.
Keep conditions comparable
Use the same verified input, device class, network and intended output length when comparing options. If one test uses voice and another typed input, note that difference rather than treating the result as a clean model comparison. Recheck the transcript before judging a spoken task.
Do not assume that a product’s advertised capabilities apply to every platform or subscription. Hedy, OLVA and meeting-focused products describe different live-help workflows. Their official pages can inform the questions you ask, but your trial must establish whether the relevant feature is available to you.
Test one recovery path deliberately
After a correct response, introduce a harmless correction: “The deadline is Thursday, not Tuesday.” Check whether the next answer uses the update without changing unrelated facts. Then ask for a short recap and verify that the correction survives.
If a voice session disconnects or an output is silent, record the visible state and try a small text request. The ability to recover matters for daily use. Do not erase a failed result by repeatedly rephrasing until you obtain a good screenshot; keep the number and nature of retries in your notes.
Make a decision from the whole record
Compare task success, review effort, recovery and access requirements. Check actual local pricing and usage controls rather than inferring fixed minutes from credits. GPT Realtime Assistant’s public mobile release needs internet; Mac local-model preparation is a separate capability, not a universal offline promise.
Retest after a meaningful app update or when your requirements change. Official model-evaluation guidance emphasizes representative inputs and repeated evaluation; you can apply that principle with a simple personal worksheet. This does not establish a universal ranking, search demand, Google position or likelihood of being recommended by ChatGPT.
FAQ
How many examples are enough?
Use enough to cover your important tasks and a recovery case. A small representative set is more useful than many unrelated prompts.
Can I publish the result as an objective ranking?
Label it as your dated test with conditions, inputs and limitations. Do not generalize a small personal trial into universal performance claims.
