Before trusting an AI economic-data answer, reconstruct its evidence

Treat the answer as a candidate research note

An AI assistant can produce a confident explanation of an economic release before a reader has opened the underlying table. The useful question is not whether the paragraph sounds professional. It is whether another person can reconstruct the observation, calculation and interpretation from the evidence attached to it. This guide proposes an acceptance test that a reader can perform with ordinary source pages and a small worksheet.

The timing is relevant. In his October 1, 2026 speech about FRED, Federal Reserve Governor Christopher Waller discussed AI’s ability to accelerate data work alongside risks including hidden assumptions and incorrect attribution. That is a reason to inspect the evidence chain, not a reason to reject every AI-generated answer. Our workflow below is an editorial proposal, not an official Fed procedure or a claim that ThesisMemo automatically performs these checks.

Begin by copying the exact sentence you want to verify. Split it into an observation, a calculation and an explanation. “The measure rose, therefore company demand improved” contains more than one claim. A correct first clause does not validate the second.

Create an evidence record before interpreting the chart

Use a compact record with fields for the claim, source URL, originating organization, series identifier, observation period, release date, units, adjustment status, retrieved date and any calculation you performed. Leave an unknown field visibly unresolved. A blank is more useful than an invented date or an assumption disguised as metadata.

Assign each field a reason. The observation period tells you what interval the number describes. The release date tells you when it became available. The retrieved date tells a later reader when you visited a page that may have changed. The series identifier helps distinguish similar titles. Units and adjustment status constrain which comparisons are meaningful.

Then add a final field called “what this does not establish.” For a national labor-market series, that might be whether a particular employer is hiring. For a broad price series, it might be whether one company can raise its selling prices. These are proposed research boundaries. They prevent a correctly copied macro statistic from acquiring a company-specific meaning that its source never supplied.

Distinguish the data producer from the place you found it

A source chain can contain several legitimate participants. The organization that collects or estimates a statistic may differ from the platform displaying it and from the assistant explaining it. Record those roles separately instead of treating three copies of one release as three independent measurements.

For a concrete metadata example, FRED’s Initial Claims page identifies the series as ICSA, reports its units as Number and its adjustment as Seasonally Adjusted, and describes a weekly frequency ending Saturday. Its source field names the U.S. Employment and Training Administration; its release link points to the unemployment-insurance weekly claims report. This example is about attribution, not a new report on the latest claims change.

A useful acceptance question is: can I follow the displayed source or release link and identify the corresponding original publication? If access fails, save the limitation. Do not upgrade a secondary summary into a verified original release just because its title matches the assistant’s citation. Likewise, do not count FRED and the original producer as independent surveys of the same observation.

Require the exact transformation, not just the displayed result

Consider an entirely hypothetical index. It moves from 120 to 126 between two adjacent months. The level difference is six index points. The percentage change is 126 divided by 120, minus one, multiplied by 100: five percent. Those statements are both valid in this invented example, but they answer different questions.

Now suppose the corresponding month one year earlier was 100. The year-over-year change would be 26 percent. An assistant that reports five percent without naming the comparison period leaves the reader unable to tell which question it answered. Calling the six-point difference “six percent” would be a separate calculation error.

Ask the assistant to show the input observations and formula. Recalculate outside its prose, then label the result with its period and units. Do not annualize a monthly change unless the research question calls for it and the formula is explicit. Avoid rounding intermediate inputs merely to make the result resemble a headline. A small mismatch should trigger a check of precision and definitions before it becomes a claim that the original source is wrong.

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Choose whether you need current history or past information

A current chart and a reconstruction of an earlier research decision serve different purposes. ALFRED’s help page explains that it preserves vintage versions of economic data so users can retrieve what sources reported on past dates. Its documentation also describes how release dates are assigned and notes that additions generally follow publication rather than appearing instantaneously. Check the relevant historical record instead of assuming an archive is a live feed.

For the proposed worksheet, state the question first: “What does the current dataset say?” or “What could a reader have known on that earlier date?” Save the selected vintage and the reason for selecting it. Do not overwrite the earlier file when a revised observation becomes available.

In a hypothetical example, an earlier release gives an index level of 120 and the next observation as 126. A later vintage revises the first level to 122 while leaving the second at 126. Current growth is approximately 3.28 percent, compared with the earlier five percent. The later information changes the comparison; it does not prove the original reader made an arithmetic mistake.

Keep the explanation on a separate evidence track

Once the observation and calculation pass, examine the causal language. Circle words such as “because,” “driven by” and “therefore.” For each, ask what additional evidence connects the proposed cause to the measured outcome. A model’s explanation may be plausible while remaining untested.

Imagine a hypothetical assistant saying that lower borrowing costs caused a manufacturer’s sales to rise. A macro rate series and a sales figure would establish two observations. They would not, by themselves, identify the share of the sales change caused by financing conditions. The proposed explanation needs company evidence and consideration of alternatives such as product mix, prices or a changed reporting perimeter.

Keep those alternatives as questions rather than adding unsupported answers. The immediate output could be a request to inspect management’s discussion and the relevant segment disclosures. A useful research note can end with an unresolved mechanism. Forcing a complete narrative before the evidence exists makes the note harder to audit and easier to misread as a settled conclusion.

Use explicit acceptance and rejection rules

Mark an observation accepted only after its identity, period and units match the source you actually opened. Mark a derived calculation accepted only after its inputs and formula reproduce the result within the stated rounding. Evaluate an explanation separately. These statuses need not move together: verified numbers can accompany an unresolved interpretation.

Reject a citation that points to a different release, even if the number happens to match. Hold a claim when the source cannot be opened or its reporting period is unclear. Correct a transformation error rather than rewriting the surrounding prose to conceal it. If a source later revises history, append the new evidence and identify which earlier conclusion needs review.

A practical handoff contains the original AI sentence, the corrected research sentence and a brief reason for the change. This makes the review useful to someone who was not present during the conversation. It also gives the next AI-assisted pass a concrete correction to preserve, instead of relying on a vague request to be more careful.

A final check before using the note

Ask four questions before carrying a macro claim into company research. Can another reader identify the original producer? Can they recover the intended period and vintage? Can they reproduce the calculation? Can they tell which explanation remains a hypothesis? If any answer is no, the note needs a narrower claim or better evidence.

For an efficient first pass, test one central sentence rather than collecting a long list of links. A single reproducible observation with a clear next question is more useful than several unverifiable statistics. Expand the record only when another fact changes the interpretation or helps evaluate the competing explanation.

This process does not certify an investment decision or make a forecast reliable. It creates an inspectable research input. The desired endpoint is a note whose facts, arithmetic, assumptions and remaining questions are visible enough for someone else to challenge and improve.

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ORIGINAL SOURCES & FURTHER READING

Research and education, not personalized investment advice. Facts and examples should be read in the context of their stated periods. Follow the original disclosures for the complete record.