Does your task need AI?

Research behind the approach: start with a simple baseline · define the right problem. These inform the design; your workflow still needs its own validation.

Your task

Examples fill the form for you to review. They are illustrations, not measured benchmarks, and never send an email or contact a customer.

These are the fields the grade is computed from. Answering them yourself lets you check every assumption before using the result.

In one sentence, what is the task? How is this work done today? Is it keeping up? Can a known procedure produce the whole answer? For example: record approved result → check status → fill template → queue email. Deciding the result is a different step. A format check alone is not a solution.

Optional detail — output, input, labels, and risk

What does it have to produce? What arrives? Do you have past cases with known correct results? These help train and test a learned model. A known notification or formula can work without them. How many possible categories or outcomes? Leave blank for open-ended answers, such as a new paragraph. Needs facts not in the input The answer depends on knowledge the input does not contain. Careful people would disagree No condition you could write down settles it. A check could settle correctness For example, compare against an approved record or check that two bookings do not overlap. What does one wrong answer cost? Severe means money, legal exposure, safety, or trust you do not get back. Can a wrong answer be undone? Does a person check the answer? Only checking every answer mitigates a severe mistake — sampling finds patterns, not instances.

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Matching benchmark

Limits and review

Tasks per month Minimum accuracy (optional) Answer needed within (ms, optional) Must be auditable A human has to be able to see why a given answer came out. Data may leave your premises Uncheck if this content cannot go to a third-party provider.

Does your task actually need AI?

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