A report schema is useful when you need to stop a weekly report from presenting interpretation as if it were an observed result. The result should make the underlying records easier to inspect and the next decision easier to explain. This guide develops a specific report schema with a source schema, operating rules, a fictional example, and observable review checks. It is intended for someone who already has the underlying material and needs a dependable way to organize it.
Product references checked: 7 October 2026. The examples and implementation specifications below are original editorial guidance. They illustrate expected behavior and have not been presented as a benchmark or as a completed tool tested inside another product.
Define the deliverable and its scope
This is a recurring reporting schema, not an automated measurement system or a generic writing tutorial.
Write the acceptance standard before asking ChatGPT for a draft. A reviewer should be able to identify the observation, inspect the evidence reference, and understand the reporting cutoff without guessing what a blank field means. Decide which records belong in the deliverable, which are out of scope, and who can confirm the final interpretation. Keep those choices visible in the working specification rather than leaving the assistant to supply convenient defaults.
Prepare the source package
Gather a dated reporting period, approved metric definitions, source extracts, and named reviewers. Assign source IDs and preserve original wording wherever a small difference could change the outcome. Record the collection date and the version of each source. If two files disagree, keep both references and mark the conflict. Do not replace a disputed value with whichever one produces the cleanest display.
Use a small representative subset for the first draft. Include one ordinary record, one incomplete record, and one record that challenges an important rule. For this task, use the following as the incomplete-record check: Delete the source for a metric: the report must show missing evidence rather than retain an unsupported number. Preserve the original package separately from the assistant's transformed output. A polished result is easier to audit when its IDs still lead back to the original supplied material.
Define the record schema
| Field | Required meaning |
|---|---|
| Observation | A statement directly supported by a supplied record, including its time period and population. |
| Evidence reference | The source ID, document location, collection date, and any unresolved verification gap. |
| Commentary | An interpretation of the observation with its assumptions and competing explanations visible. |
| Proposed action | A suggestion with an owner only when the owner is supplied or explicitly approved. |
| Decision status | Distinguish proposed, accepted, rejected, and pending rather than implying agreement. |
| Reporting cutoff | The date and timezone through which new evidence was included in this report. |
Treat observation as a deliberate part of the data model. A statement directly supported by a supplied record, including its time period and population. Keep its representation consistent when you sort, filter, or revise the output. The evidence reference field has a different job: The source ID, document location, collection date, and any unresolved verification gap. Combining these fields into one sentence can hide the distinction the reviewer needs to inspect.
Pay particular attention to commentary and proposed action. The former means: An interpretation of the observation with its assumptions and competing explanations visible. The latter means: A suggestion with an owner only when the owner is supplied or explicitly approved. Store them separately and label both in the detail view or report. This makes disagreements visible at the exact point where they affect the result instead of burying them in a general note at the bottom.
Apply the task rules
Rule 1. Keep absolute counts alongside percentages so a change in population cannot masquerade as an improvement.
Rule 2. Do not infer causality from two metrics moving together. Put causal explanations in commentary and identify the missing evidence.
Rule 3. If the metric definition changed, flag the comparison as noncomparable until the owner approves a restatement.
Rule 4. Carry open decisions forward by stable ID while removing actions only after explicit closure evidence is recorded.
Make these rules inspectable alongside the report schema. Ask ChatGPT to describe how each rule affects a record and what should happen when the supplied data does not establish an answer. A rule written in prose still needs a defined output state. Use an explicit missing, disputed, or not-applicable label where appropriate, and reserve a valid zero or an empty result for cases where it has a defined meaning.
Work through a fictional example
A fictional support team records 120 tickets in Week A and 100 in Week B. Its observation is that the count decreased by 20. Commentary may propose that a help-center update contributed, but the records do not establish that cause. If staffing, customer volume, or ticket categorization changed, those are additional hypotheses. A useful action is to review the category mix, rather than declare the help-center update successful. The report should state whether both weeks use the same ticket definition and timezone.
Use the example as a review fixture rather than as real operational data. Before generating the output, write down which fields should be populated and which should remain unresolved. Then compare the generated result with that expectation. If ChatGPT supplies an unsupported value, correct the underlying instruction or calculation rule instead of merely editing the one visible record. Otherwise, the same error may return when the next record is processed.
Build the first version
Ask ChatGPT for an extraction table before asking for a polished report schema. Review a few source references against the original material and correct the table first. Then request the reader-facing output using the approved fields. Keep changes to the source document separate from this drafting pass so a mistaken extraction does not automatically overwrite the authoritative record.
The final review should explicitly cover decision status: Distinguish proposed, accepted, rejected, and pending rather than implying agreement. Also check reporting cutoff: The date and timezone through which new evidence was included in this report. These two fields often establish whether a reader can act on the result. Make missing information visible beside the affected record and ask the owner for the exact evidence needed to resolve it.
A reusable prompt
Create a report schema from the supplied source package. Goal: stop a weekly report from presenting interpretation as if it were an observed result. Use these fields: Observation: A statement directly supported by a supplied record, including its time period and population.; Evidence reference: The source ID, document location, collection date, and any unresolved verification gap.; Commentary: An interpretation of the observation with its assumptions and competing explanations visible.; Proposed action: A suggestion with an owner only when the owner is supplied or explicitly approved.; Decision status: Distinguish proposed, accepted, rejected, and pending rather than implying agreement.; Reporting cutoff: The date and timezone through which new evidence was included in this report.. Preserve source IDs. Rules: Keep absolute counts alongside percentages so a change in population cannot masquerade as an improvement. Do not infer causality from two metrics moving together. Put causal explanations in commentary and identify the missing evidence. If the metric definition changed, flag the comparison as noncomparable until the owner approves a restatement. Carry open decisions forward by stable ID while removing actions only after explicit closure evidence is recorded.. First explain the proposed data model and unresolved inputs. Then build the smallest usable version. Use only supplied facts; label the demonstration fictional. Do not invent approvals, thresholds, owners, or external integrations. Include an empty state and explain the expected result for these checks: Delete the source for a metric: the report must show missing evidence rather than retain an unsupported number. Change the reporting population: the comparison must expose the scope change. Supply a disputed explanation: keep it in commentary with attribution, not in the observation field.
Review behavior and edge cases
Check 1. Delete the source for a metric: the report must show missing evidence rather than retain an unsupported number. Record the observed result beside the expected result. If the check fails, isolate the affected rule and request a focused correction before rerunning the same case.
Check 2. Change the reporting population: the comparison must expose the scope change. Record the observed result beside the expected result. If the check fails, isolate the affected rule and request a focused correction before rerunning the same case.
Check 3. Supply a disputed explanation: keep it in commentary with attribution, not in the observation field. Record the observed result beside the expected result. If the check fails, isolate the affected rule and request a focused correction before rerunning the same case.
Repeat the checks after adding another source record or changing the presentation. A working first example does not prove every record is handled correctly. Pay attention to retained selections, stale derived values, and unexplained changes in the number of displayed records. For report schema work, the reviewer must be able to tell whether a difference comes from new evidence, an edited rule, or merely a changed view.
Maintain the reviewed version
Keep a version label, source cutoff, and short change record with the finished report schema. Record who approved the rules and which demonstration cases were reviewed. When the source package changes, preserve record IDs and revisit the checks affected by the change. Do not treat yesterday's verified result as evidence that today's replacement input is complete.
Before handing the output to another person, review its audience and the material it contains. Use a sanitized demonstration for general feedback when the real source package is confidential. Explain whether changes are saved, whether the output is a preview, and which source remains authoritative. If persistence or sharing is required, verify that behavior in the actual account and intended environment instead of assuming it from a successful preview.
Frequently asked questions
Can ChatGPT fill missing information automatically?
For this task, missing values should remain visible. Keep absolute counts alongside percentages so a change in population cannot masquerade as an improvement. Ask for the evidence needed to complete the record instead of letting a plausible substitute become an apparent fact.
What should happen when the source records disagree?
Keep the conflicting records with their identifiers. Do not infer causality from two metrics moving together. Put causal explanations in commentary and identify the missing evidence. Ask the responsible owner to resolve the discrepancy or leave the affected outcome unavailable when it depends on the disputed information.
What is the smallest useful review example?
A fictional support team records 120 tickets in Week A and 100 in Week B. Add the missing or conflicting case described above so the review covers more than a clean successful record.
How do I know the first version is ready?
The required fields are present, the rules are visible, and the three listed checks produce their expected outcomes. In particular: Supply a disputed explanation: keep it in commentary with attribution, not in the observation field. Review the result in the intended reading or interaction environment.
Should the assistant replace the original source?
Keep the source authoritative and review the transformed output separately. This is a recurring reporting schema, not an automated measurement system or a generic writing tutorial. Save revisions with a source cutoff so another reviewer can reconstruct which material produced the current result.
Sources and related reading
Consult the current primary references for account-specific availability and controls: OpenAI file documentation, OpenAI data controls. The schema, examples, prompts, and review checks here are editorial recommendations rather than documented built-in features.
For broader context, read the related ChatGPT workflow guide.
