Purpose
Use this lesson to turn messy data symptoms into explicit, repeatable transformations. The aim is to produce work that a colleague can review and act on, not merely text that sounds convincing.
Lesson Explanation
Scenario: A customer file has duplicate emails and inconsistent country names.
Better instruction: Propose cleaning rules: trim spaces, standardize country names to an approved list, flag duplicate emails, and preserve the original value in a separate column.
Data cleaning must be reversible and auditable. Define source columns, transformation rules, approved values, and what to flag rather than overwrite. Ask the AI for rules and test cases before applying changes to production data. Original values remain available, duplicates are flagged, and countries follow an approved list.
Work in a short cycle: provide the relevant input, state the result and limits, inspect the draft against the acceptance criteria, then revise the instruction when a requirement is missing. Keep the final human decision with the person responsible for the work.
Practice Questions
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What is the main work outcome in “Designing Data-Cleaning Rules”?
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turn messy data symptoms into explicit, repeatable transformations.
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Why is this request incomplete: A customer file has duplicate emails and inconsistent country names.
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It leaves important decisions to guesswork. The lesson shows how to supply the missing purpose, context, boundary, or format.
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Which instruction makes the request operational: Propose cleaning rules: trim spaces, standardize country names to an approved list, flag duplicate emails, and preserve the original value in a separate column.
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It defines a concrete result that can be checked instead of asking for a vague response.
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What principle should guide your prompt for this lesson?
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Data cleaning must be reversible and auditable. Define source columns, transformation rules, approved values, and what to flag rather than overwrite. Ask the AI for rules and test cases before applying changes to production data.
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What would count as an acceptable result here?
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Original values remain available, duplicates are flagged, and countries follow an approved list.
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Name one detail you would verify before using an AI result for “Designing Data-Cleaning Rules”.
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Verify the source facts, inputs, numbers, names, dates, policy limits, or assumptions that affect the real decision.
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What should you add if the result is polished but not usable for the scenario in this lesson?
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Add the missing acceptance criteria or output structure, then regenerate and compare against the stated requirement.
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Which is safer: asking for a general answer or stating the business context in this lesson? Why?
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State the business context because it reduces irrelevant guesses and lets the AI tailor the work to the actual situation.
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How would you test the output from “Designing Data-Cleaning Rules” before sharing it?
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Check it against the requested facts, format, limits, and acceptance criteria; then have the appropriate human reviewer approve it.
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What should the AI do when a required fact is missing in this scenario?
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Flag the missing fact or ask a focused question rather than silently inventing an answer.
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What risk does the lesson warn about for “Designing Data-Cleaning Rules”?
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Treating fluent output as automatically correct, complete, approved, or fit for real-world use.
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Write the shortest useful improvement to the weak request in this lesson.
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Propose cleaning rules: trim spaces, standardize country names to an approved list, flag duplicate emails, and preserve the original value in a separate column.