Quality and trust
Working thoughtfully with AI: verify answers and handle data carefully
A practical checklist for AI work: share only necessary information, identify uncertain claims and review the output before putting it to use.
Do not confuse fluency with accuracy
An AI model can produce a coherent answer that contains mistakes. A source name, date, number or software function may be inaccurate. Confidence should come from being able to check the answer, rather than from the certainty of its tone.
Identify claims that can be verified as you read. If the response cites a source, open the source and check whether it supports the actual statement. Asking for references is useful, but a link in an answer is only a starting point for verification.
Provide only the information the task requires
Most tasks do not require an entire document or a complete raw dataset. Select the relevant material and remove unnecessary names, identifiers, keys and other details. For a software error, a small reproducible example is often more useful than sharing the whole repository.
Before sharing organizational information, review your organization’s rules and the terms of the service and model provider. Do not infer how information is processed, stored or used from the appearance of an interface. Those questions depend on the service, settings and applicable agreements.
Match the review to the task
For a summary, compare the output against the original and check that conditions and exceptions were retained. For a calculation, verify the numbers independently. For code, run relevant tests and type checks. For public content, check the claims and suitability of the tone.
Ask the model to identify assumptions and missing information. That can make uncertainty more visible, but it does not replace independent review. If the answer rests on incomplete input, supply the missing information before assessing the conclusion again.
Try this example
Review this response. List verifiable claims, assumptions and details that were not supplied in the input separately. Do not present uncertain points as settled facts. Explain what evidence would be needed to confirm each one.
Pause briefly before using the result
Before publishing or executing a result, ask three questions: have I shared unnecessary sensitive information? Have I checked the important claims? Do I understand the assumptions behind the answer? This short pause is a practical part of a careful workflow.
Ditfa can help explain concepts, draft text and explore options. The quality of the result depends on your input, chosen model and review. Keep clear prompts together with a method for checking their outputs; both belong in a sustainable way of working with AI.
Put it into practice with a real task
Ask a specific question, provide the context and improve the answer one step at a time.
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